The German cervical cancer screening model: development and validation of a decision-analytic model for cervical cancer screening in Germany.

PubMed

Siebert, Uwe; Sroczynski, Gaby; Hillemanns, Peter; Engel, Jutta; Stabenow, Roland; Stegmaier, Christa; Voigt, Kerstin; Gibis, Bernhard; Hölzel, Dieter; Goldie, Sue J

2006-04-01

We sought to develop and validate a decision-analytic model for the natural history of cervical cancer for the German health care context and to apply it to cervical cancer screening. We developed a Markov model for the natural history of cervical cancer and cervical cancer screening in the German health care context. The model reflects current German practice standards for screening, diagnostic follow-up and treatment regarding cervical cancer and its precursors. Data for disease progression and cervical cancer survival were obtained from the literature and German cancer registries. Accuracy of Papanicolaou (Pap) testing was based on meta-analyses. We performed internal and external model validation using observed epidemiological data for unscreened women from different German cancer registries. The model predicts life expectancy, incidence of detected cervical cancer cases, lifetime cervical cancer risks and mortality. The model predicted a lifetime cervical cancer risk of 3.0% and a lifetime cervical cancer mortality of 1.0%, with a peak cancer incidence of 84/100,000 at age 51 years. These results were similar to observed data from German cancer registries, German literature data and results from other international models. Based on our model, annual Pap screening could prevent 98.7% of diagnosed cancer cases and 99.6% of deaths due to cervical cancer in women completely adherent to screening and compliant to treatment. Extending the screening interval from 1 year to 2, 3 or 5 years resulted in reduced screening effectiveness. This model provides a tool for evaluating the long-term effectiveness of different cervical cancer screening tests and strategies.

Modeling Individual Patient Preferences for Colorectal Cancer Screening Based on Their Tolerance for Complications Risk.

PubMed

Taksler, Glen B; Perzynski, Adam T; Kattan, Michael W

2017-04-01

Recommendations for colorectal cancer screening encourage patients to choose among various screening methods based on individual preferences for benefits, risks, screening frequency, and discomfort. We devised a model to illustrate how individuals with varying tolerance for screening complications risk might decide on their preferred screening strategy. We developed a discrete-time Markov mathematical model that allowed hypothetical individuals to maximize expected lifetime utility by selecting screening method, start age, stop age, and frequency. Individuals could choose from stool-based testing every 1 to 3 years, flexible sigmoidoscopy every 1 to 20 years with annual stool-based testing, colonoscopy every 1 to 20 years, or no screening. We compared the life expectancy gained from the chosen strategy with the life expectancy available from a benchmark strategy of decennial colonoscopy. For an individual at average risk of colorectal cancer who was risk neutral with respect to screening complications (and therefore was willing to undergo screening if it would actuarially increase life expectancy), the model predicted that he or she would choose colonoscopy every 10 years, from age 53 to 73 years, consistent with national guidelines. For a similar individual who was moderately averse to screening complications risk (and therefore required a greater increase in life expectancy to accept potential risks of colonoscopy), the model predicted that he or she would prefer flexible sigmoidoscopy every 12 years with annual stool-based testing, with 93% of the life expectancy benefit of decennial colonoscopy. For an individual with higher risk aversion, the model predicted that he or she would prefer 2 lifetime flexible sigmoidoscopies, 20 years apart, with 70% of the life expectancy benefit of decennial colonoscopy. Mathematical models may formalize how individuals with different risk attitudes choose between various guideline-recommended colorectal cancer screening strategies.

Communication: Introducing prescribed biases in out-of-equilibrium Markov models

NASA Astrophysics Data System (ADS)

Dixit, Purushottam D.

2018-03-01

Markov models are often used in modeling complex out-of-equilibrium chemical and biochemical systems. However, many times their predictions do not agree with experiments. We need a systematic framework to update existing Markov models to make them consistent with constraints that are derived from experiments. Here, we present a framework based on the principle of maximum relative path entropy (minimum Kullback-Leibler divergence) to update Markov models using stationary state and dynamical trajectory-based constraints. We illustrate the framework using a biochemical model network of growth factor-based signaling. We also show how to find the closest detailed balanced Markov model to a given Markov model. Further applications and generalizations are discussed.

Continuous-Time Semi-Markov Models in Health Economic Decision Making: An Illustrative Example in Heart Failure Disease Management.

PubMed

Cao, Qi; Buskens, Erik; Feenstra, Talitha; Jaarsma, Tiny; Hillege, Hans; Postmus, Douwe

2016-01-01

Continuous-time state transition models may end up having large unwieldy structures when trying to represent all relevant stages of clinical disease processes by means of a standard Markov model. In such situations, a more parsimonious, and therefore easier-to-grasp, model of a patient's disease progression can often be obtained by assuming that the future state transitions do not depend only on the present state (Markov assumption) but also on the past through time since entry in the present state. Despite that these so-called semi-Markov models are still relatively straightforward to specify and implement, they are not yet routinely applied in health economic evaluation to assess the cost-effectiveness of alternative interventions. To facilitate a better understanding of this type of model among applied health economic analysts, the first part of this article provides a detailed discussion of what the semi-Markov model entails and how such models can be specified in an intuitive way by adopting an approach called vertical modeling. In the second part of the article, we use this approach to construct a semi-Markov model for assessing the long-term cost-effectiveness of 3 disease management programs for heart failure. Compared with a standard Markov model with the same disease states, our proposed semi-Markov model fitted the observed data much better. When subsequently extrapolating beyond the clinical trial period, these relatively large differences in goodness-of-fit translated into almost a doubling in mean total cost and a 60-d decrease in mean survival time when using the Markov model instead of the semi-Markov model. For the disease process considered in our case study, the semi-Markov model thus provided a sensible balance between model parsimoniousness and computational complexity. © The Author(s) 2015.

Semi-Markov adjunction to the Computer-Aided Markov Evaluator (CAME)

NASA Technical Reports Server (NTRS)

Rosch, Gene; Hutchins, Monica A.; Leong, Frank J.; Babcock, Philip S., IV

1988-01-01

The rule-based Computer-Aided Markov Evaluator (CAME) program was expanded in its ability to incorporate the effect of fault-handling processes into the construction of a reliability model. The fault-handling processes are modeled as semi-Markov events and CAME constructs and appropriate semi-Markov model. To solve the model, the program outputs it in a form which can be directly solved with the Semi-Markov Unreliability Range Evaluator (SURE) program. As a means of evaluating the alterations made to the CAME program, the program is used to model the reliability of portions of the Integrated Airframe/Propulsion Control System Architecture (IAPSA 2) reference configuration. The reliability predictions are compared with a previous analysis. The results bear out the feasibility of utilizing CAME to generate appropriate semi-Markov models to model fault-handling processes.

Hospitalized Patients with Cirrhosis Should Be Screened for Clostridium difficile Colitis.

PubMed

Saab, Sammy; Alper, Theodore; Sernas, Ernesto; Pruthi, Paridhima; Alper, Mikhail A; Sundaram, Vinay

2015-10-01

Clostridium difficile infection (CDI) is an important public health problem in hospitalized patients. Patients with cirrhosis are particularly at risk of increased associated morbidity, mortality, and healthcare utilization from CDI. The aim of this study was to assess the pharmacoeconomic impact of CDI screening on hospitalized patients with cirrhosis. A Markov model was used to compare costs and outcomes of two strategies for the screening of CDI. The first strategy consisted of screening all patients for CDI and treating if detected (screening). In the second strategy, only patients found to have symptomatic CDI were treated (no screening). The probability of underlying CDI prevalence, symptomatic CDI infection, and likelihood of recurrent infection were varied in a sensitivity analysis. The costs of antibiotics and hospitalization were also assessed. Differences in outcome were expressed in ratio of the total costs associated with screening to the total costs associated without screening. The results of our model showed that screening for CDI was consistently associated with improved healthcare outcomes and decreased healthcare utilization across all variables in the one- and two-way sensitivity analyses. Using baseline assumptions, the costs associated with the no screening strategy were 3.54 times that of the screening strategy. Moreover, the mortality for symptomatic CDI was lower in the screening strategy than the no screening strategy. The screening strategy results in less healthcare utilization and improved clinical outcomes. Screening for CDI measures favorably.

Cost-Effectiveness of Three Rounds of Mammography Breast Cancer Screening in Iranian Women

PubMed Central

Haghighat, Shahpar; Akbari, Mohammad Esmaeil; Yavari, Parvin; Javanbakht, Mehdi; Ghaffari, Shahram

2016-01-01

Background Breast cancer is the most common cancer in Iranian women as is worldwide. Mammography screening has been introduced as a beneficial method for reducing mortality and morbidity of this disease. Objectives We developed an analytical model to assess the cost effectiveness of an organized mammography screening program in Iran for early detection of the breast cancer. Patients and Methods This study is an economic evaluation of mammography screening program among Iranian woman aged 40 - 70 years. A decision tree and Markov model were applied to estimate total quality adjusted life years (QALY) and lifetime costs. Results The results revealed that the incremental cost effectiveness ratio (ICER) of mammography screening in Iranian women in the first round was Int. $ 37,350 per QALY gained. The model showed that the ICER in the second and third rounds of screening program were Int. $ 141,641 and Int. $ 389,148 respectively. Conclusions Study results identified that mammography screening program was cost-effective in 53% of the cases, but incremental cost per QALY in the second and third rounds of screening are much higher than the accepted payment threshold of Iranian health system. Thus, evaluation of other screening strategies would be useful to identify more cost-effective program. Future studies with new national data can improve the accuracy of our finding and provide better information for health policy makers for decision making. PMID:27366315

Colonoscopy video quality assessment using hidden Markov random fields

NASA Astrophysics Data System (ADS)

Park, Sun Young; Sargent, Dusty; Spofford, Inbar; Vosburgh, Kirby

2011-03-01

With colonoscopy becoming a common procedure for individuals aged 50 or more who are at risk of developing colorectal cancer (CRC), colon video data is being accumulated at an ever increasing rate. However, the clinically valuable information contained in these videos is not being maximally exploited to improve patient care and accelerate the development of new screening methods. One of the well-known difficulties in colonoscopy video analysis is the abundance of frames with no diagnostic information. Approximately 40% - 50% of the frames in a colonoscopy video are contaminated by noise, acquisition errors, glare, blur, and uneven illumination. Therefore, filtering out low quality frames containing no diagnostic information can significantly improve the efficiency of colonoscopy video analysis. To address this challenge, we present a quality assessment algorithm to detect and remove low quality, uninformative frames. The goal of our algorithm is to discard low quality frames while retaining all diagnostically relevant information. Our algorithm is based on a hidden Markov model (HMM) in combination with two measures of data quality to filter out uninformative frames. Furthermore, we present a two-level framework based on an embedded hidden Markov model (EHHM) to incorporate the proposed quality assessment algorithm into a complete, automated diagnostic image analysis system for colonoscopy video.

Cost-effectiveness of breast cancer screening policies using simulation.

PubMed

Gocgun, Y; Banjevic, D; Taghipour, S; Montgomery, N; Harvey, B J; Jardine, A K S; Miller, A B

2015-08-01

In this paper, we study breast cancer screening policies using computer simulation. We developed a multi-state Markov model for breast cancer progression, considering both the screening and treatment stages of breast cancer. The parameters of our model were estimated through data from the Canadian National Breast Cancer Screening Study as well as data in the relevant literature. Using computer simulation, we evaluated various screening policies to study the impact of mammography screening for age-based subpopulations in Canada. We also performed sensitivity analysis to examine the impact of certain parameters on number of deaths and total costs. The analysis comparing screening policies reveals that a policy in which women belonging to the 40-49 age group are not screened, whereas those belonging to the 50-59 and 60-69 age groups are screened once every 5 years, outperforms others with respect to cost per life saved. Our analysis also indicates that increasing the screening frequencies for the 50-59 and 60-69 age groups decrease mortality, and that the average number of deaths generally decreases with an increase in screening frequency. We found that screening annually for all age groups is associated with the highest costs per life saved. Our analysis thus reveals that cost per life saved increases with an increase in screening frequency. Copyright © 2015 Elsevier Ltd. All rights reserved.

[Cost-effectiveness analysis of universal screening for thyroid disease in pregnant women in Spain].

PubMed

Donnay Candil, Sergio; Balsa Barro, José Antonio; Álvarez Hernández, Julia; Crespo Palomo, Carlos; Pérez-Alcántara, Ferrán; Polanco Sánchez, Carlos

2015-01-01

To assess the cost-effectiveness of universal screening for thyroid disease in pregnant women in Spain as compared to high risk screening and no screening. A decision-analytic model comparing the incremental cost per quality-adjusted life year (QALY) of universal screening versus high risk screening and versus no screening. was used for the pregnancy and postpartum period. Probabilities from randomized controlled trials were considered for adverse obstetrical outcomes. A Markov model was used to assess the lifetime period after the first postpartum year and account for development of overt hypothyroidism. The main assumptions in the model and use of resources were assessed by local clinical experts. The analysis considered direct healthcare costs only. Universal screening gained .011 QALYs over high risk screening and .014 QALYS over no screening. Total direct costs per patient were €5,786 for universal screening, €5,791 for high risk screening, and €5,781 for no screening. Universal screening was dominant compared to risk-based screening and a very cost-effective alternative as compared to no screening. Use of universal screening instead of high risk screening would result in €2,653,854 annual savings for the Spanish National Health System. Universal screening for thyroid disease in pregnant women in the first trimester is dominant in Spain as compared to risk-based screening, and is cost-effective as compared to no screening (incremental cost-effectiveness ratio of €374 per QALY). Moreover, it allows diagnosing and treating cases of clinical and subclinical hypothyroidism that may not be detected when only high-risk women are screened. Copyright © 2014 SEEN. Published by Elsevier España, S.L.U. All rights reserved.

A Bayesian model for estimating multi-state disease progression.

PubMed

Shen, Shiwen; Han, Simon X; Petousis, Panayiotis; Weiss, Robert E; Meng, Frank; Bui, Alex A T; Hsu, William

2017-02-01

A growing number of individuals who are considered at high risk of cancer are now routinely undergoing population screening. However, noted harms such as radiation exposure, overdiagnosis, and overtreatment underscore the need for better temporal models that predict who should be screened and at what frequency. The mean sojourn time (MST), an average duration period when a tumor can be detected by imaging but with no observable clinical symptoms, is a critical variable for formulating screening policy. Estimation of MST has been long studied using continuous Markov model (CMM) with Maximum likelihood estimation (MLE). However, a lot of traditional methods assume no observation error of the imaging data, which is unlikely and can bias the estimation of the MST. In addition, the MLE may not be stably estimated when data is sparse. Addressing these shortcomings, we present a probabilistic modeling approach for periodic cancer screening data. We first model the cancer state transition using a three state CMM model, while simultaneously considering observation error. We then jointly estimate the MST and observation error within a Bayesian framework. We also consider the inclusion of covariates to estimate individualized rates of disease progression. Our approach is demonstrated on participants who underwent chest x-ray screening in the National Lung Screening Trial (NLST) and validated using posterior predictive p-values and Pearson's chi-square test. Our model demonstrates more accurate and sensible estimates of MST in comparison to MLE. Copyright © 2016 Elsevier Ltd. All rights reserved.

Derivation of Markov processes that violate detailed balance

NASA Astrophysics Data System (ADS)

Lee, Julian

2018-03-01

Time-reversal symmetry of the microscopic laws dictates that the equilibrium distribution of a stochastic process must obey the condition of detailed balance. However, cyclic Markov processes that do not admit equilibrium distributions with detailed balance are often used to model systems driven out of equilibrium by external agents. I show that for a Markov model without detailed balance, an extended Markov model can be constructed, which explicitly includes the degrees of freedom for the driving agent and satisfies the detailed balance condition. The original cyclic Markov model for the driven system is then recovered as an approximation at early times by summing over the degrees of freedom for the driving agent. I also show that the widely accepted expression for the entropy production in a cyclic Markov model is actually a time derivative of an entropy component in the extended model. Further, I present an analytic expression for the entropy component that is hidden in the cyclic Markov model.

On Markov parameters in system identification

NASA Technical Reports Server (NTRS)

Phan, Minh; Juang, Jer-Nan; Longman, Richard W.

1991-01-01

A detailed discussion of Markov parameters in system identification is given. Different forms of input-output representation of linear discrete-time systems are reviewed and discussed. Interpretation of sampled response data as Markov parameters is presented. Relations between the state-space model and particular linear difference models via the Markov parameters are formulated. A generalization of Markov parameters to observer and Kalman filter Markov parameters for system identification is explained. These extended Markov parameters play an important role in providing not only a state-space realization, but also an observer/Kalman filter for the system of interest.

Cost effectiveness of fecal DNA screening for colorectal cancer: a systematic review and quality appraisal of the literature.

PubMed

Skally, Mairead; Hanly, Paul; Sharp, Linda

2013-06-01

Fecal DNA (fDNA) testing is a noninvasive potential alternative to current colorectal cancer screening tests. We conducted a systematic review and quality assessment of studies of cost-effectiveness of fDNA as a colorectal cancer screening tool (compared with no screening and other screening modalities), and identified key variables that impinged on cost-effectiveness. We searched MEDLINE, Embase, and the Centre for Reviews and Dissemination for cost-effectiveness studies of fDNA-based screening, published in English by September 2011. Studies that undertook an economic evaluation of fDNA, using either a cost-effectiveness or cost-utility analysis, compared with other relevant screening modalities and/or no screening were included. Additional inclusion criteria related to the presentation of data pertaining to model variables including time horizon, costs, fDNA performance characteristics, screening uptake, and comparators. A total of 369 articles were initially identified for review. After removing duplicates and applying inclusion and exclusion criteria, seven articles were included in the final review. Data was abstracted on key descriptor variables including screening scenarios, time horizon, costs, test performance characteristics, screening uptake, comparators, and incremental cost-effectiveness ratios. Quality assessment was undertaken using a standard checklist for economic evaluations. Studies cited by cost-effectiveness articles as the source of data on fDNA test performance characteristics were also reviewed. Seven cost-effectiveness studies were included, from the USA (4), Canada (1), Israel (1), and Taiwan (1). Markov models (5), a partially observable Markov decision process model (1) and MISCAN and SimCRC (1) microsimulation models were used. All studies took a third-party payer perspective and one included, in addition, a societal perspective. Comparator screening tests, screening intervals, and specific fDNA tests varied between studies. fDNA sensitivity and specificity parameters were derived from 12 research studies and one meta-analysis. Outcomes assessed were life-years gained and quality-adjusted life-years gained. fDNA was cost-effective when compared with no screening in six studies. Compared with other screening modalities, fDNA was not considered cost-effective in any of the base-case analyses: in five studies it was dominated by all alternatives considered. Sensitivity analyses identified cost, compliance, and test parameters as key influential parameters. In general, poor presentation of "study design" and "data collection" details lowered the quality of included articles. Although the literature searches were designed for high sensitivity, the possibility cannot be excluded that some eligible studies may have been missed. Reports (such as Health Technology Assessments produced by government agencies) and other forms of grey literature were excluded because they are difficult to identify systematically and/or may not report methods and results in sufficient detail for assessment. On the basis of the available (albeit limited) evidence, while fDNA is cost-effective when compared with no screening, it is currently dominated by most of the other available screening options. Cost and test performance appear to be the main influences on cost-effectiveness.

A model to evaluate the costs and clinical effectiveness of human papilloma virus screening compared with annual papanicolaou cytology in Germany.

PubMed

Petry, Karl Ulrich; Barth, Cordula; Wasem, Jürgen; Neumann, Anja

2017-05-01

We modelled human papilloma virus (HPV) primary screening scenarios compared with Pap cytology to evaluate clinical effectiveness and projected annual costs in Germany. A Markov cohort model was built to compare the budget impact of annual Pap cytology with different 5-yearly HPV screening scenarios: (1) a positive HPV test followed by Pap cytology; (2) a positive HPV test followed by p16/Ki-67 dual-stained cytology; (3) a positive HPV test followed by colposcopy if HPV-16/18-positive or p16/Ki-67 dual-stained cytology if positive for other subtypes; (4) co-testing with HPV and Pap. Screening scenarios were based on a 10-year horizon. All HPV screening scenarios in the model were associated with fewer deaths from missed diagnosis of cervical cancer compared with Pap screening; 10-year totals n=172-344 (1.5-3 per 100,000) versus n=477 (4.1 per 100,000), respectively. Total annual costs were lower with HPV screening than Pap cytology. The projected average annual cost for HPV screening ranged from €117 million to €136 million compared with €177 million for Pap screening, representing annual savings of €41-60 million. The greatest clinical impact was achieved with primary HPV screening (with genotyping) followed by colposcopy for HPV 16/18-positive women or p16/Ki-67 dual-stained cytology for women positive for other HPV subtypes. Screening strategies including primary HPV testing for high-risk subtypes (HPV-16/18) in conjunction with p16/Ki-67 dual-stained cytology can improve the detection of cervical cancer at a lower total annual cost than conventional Pap cytology screening. Copyright © 2017. Published by Elsevier B.V.

Statistical Analysis of Notational AFL Data Using Continuous Time Markov Chains

PubMed Central

Meyer, Denny; Forbes, Don; Clarke, Stephen R.

2006-01-01

Animal biologists commonly use continuous time Markov chain models to describe patterns of animal behaviour. In this paper we consider the use of these models for describing AFL football. In particular we test the assumptions for continuous time Markov chain models (CTMCs), with time, distance and speed values associated with each transition. Using a simple event categorisation it is found that a semi-Markov chain model is appropriate for this data. This validates the use of Markov Chains for future studies in which the outcomes of AFL matches are simulated. Key Points A comparison of four AFL matches suggests similarity in terms of transition probabilities for events and the mean times, distances and speeds associated with each transition. The Markov assumption appears to be valid. However, the speed, time and distance distributions associated with each transition are not exponential suggesting that semi-Markov model can be used to model and simulate play. Team identified events and directions associated with transitions are required to develop the model into a tool for the prediction of match outcomes. PMID:24357946

Statistical Analysis of Notational AFL Data Using Continuous Time Markov Chains.

PubMed

Meyer, Denny; Forbes, Don; Clarke, Stephen R

2006-01-01

Animal biologists commonly use continuous time Markov chain models to describe patterns of animal behaviour. In this paper we consider the use of these models for describing AFL football. In particular we test the assumptions for continuous time Markov chain models (CTMCs), with time, distance and speed values associated with each transition. Using a simple event categorisation it is found that a semi-Markov chain model is appropriate for this data. This validates the use of Markov Chains for future studies in which the outcomes of AFL matches are simulated. Key PointsA comparison of four AFL matches suggests similarity in terms of transition probabilities for events and the mean times, distances and speeds associated with each transition.The Markov assumption appears to be valid.However, the speed, time and distance distributions associated with each transition are not exponential suggesting that semi-Markov model can be used to model and simulate play.Team identified events and directions associated with transitions are required to develop the model into a tool for the prediction of match outcomes.

Modeling Hubble Space Telescope flight data by Q-Markov cover identification

NASA Technical Reports Server (NTRS)

Liu, K.; Skelton, R. E.; Sharkey, J. P.

1992-01-01

A state space model for the Hubble Space Telescope under the influence of unknown disturbances in orbit is presented. This model was obtained from flight data by applying the Q-Markov covariance equivalent realization identification algorithm. This state space model guarantees the match of the first Q-Markov parameters and covariance parameters of the Hubble system. The flight data were partitioned into high- and low-frequency components for more efficient Q-Markov cover modeling, to reduce some computational difficulties of the Q-Markov cover algorithm. This identification revealed more than 20 lightly damped modes within the bandwidth of the attitude control system. Comparisons with the analytical (TREETOPS) model are also included.

A dynamic multi-scale Markov model based methodology for remaining life prediction

NASA Astrophysics Data System (ADS)

Yan, Jihong; Guo, Chaozhong; Wang, Xing

2011-05-01

The ability to accurately predict the remaining life of partially degraded components is crucial in prognostics. In this paper, a performance degradation index is designed using multi-feature fusion techniques to represent deterioration severities of facilities. Based on this indicator, an improved Markov model is proposed for remaining life prediction. Fuzzy C-Means (FCM) algorithm is employed to perform state division for Markov model in order to avoid the uncertainty of state division caused by the hard division approach. Considering the influence of both historical and real time data, a dynamic prediction method is introduced into Markov model by a weighted coefficient. Multi-scale theory is employed to solve the state division problem of multi-sample prediction. Consequently, a dynamic multi-scale Markov model is constructed. An experiment is designed based on a Bently-RK4 rotor testbed to validate the dynamic multi-scale Markov model, experimental results illustrate the effectiveness of the methodology.

Markov switching multinomial logit model: An application to accident-injury severities.

PubMed

Malyshkina, Nataliya V; Mannering, Fred L

2009-07-01

In this study, two-state Markov switching multinomial logit models are proposed for statistical modeling of accident-injury severities. These models assume Markov switching over time between two unobserved states of roadway safety as a means of accounting for potential unobserved heterogeneity. The states are distinct in the sense that in different states accident-severity outcomes are generated by separate multinomial logit processes. To demonstrate the applicability of the approach, two-state Markov switching multinomial logit models are estimated for severity outcomes of accidents occurring on Indiana roads over a four-year time period. Bayesian inference methods and Markov Chain Monte Carlo (MCMC) simulations are used for model estimation. The estimated Markov switching models result in a superior statistical fit relative to the standard (single-state) multinomial logit models for a number of roadway classes and accident types. It is found that the more frequent state of roadway safety is correlated with better weather conditions and that the less frequent state is correlated with adverse weather conditions.

Grey-Markov prediction model based on background value optimization and central-point triangular whitenization weight function

NASA Astrophysics Data System (ADS)

Ye, Jing; Dang, Yaoguo; Li, Bingjun

2018-01-01

Grey-Markov forecasting model is a combination of grey prediction model and Markov chain which show obvious optimization effects for data sequences with characteristics of non-stationary and volatility. However, the state division process in traditional Grey-Markov forecasting model is mostly based on subjective real numbers that immediately affects the accuracy of forecasting values. To seek the solution, this paper introduces the central-point triangular whitenization weight function in state division to calculate possibilities of research values in each state which reflect preference degrees in different states in an objective way. On the other hand, background value optimization is applied in the traditional grey model to generate better fitting data. By this means, the improved Grey-Markov forecasting model is built. Finally, taking the grain production in Henan Province as an example, it verifies this model's validity by comparing with GM(1,1) based on background value optimization and the traditional Grey-Markov forecasting model.

Markov models in dentistry: application to resin-bonded bridges and review of the literature.

PubMed

Mahl, Dominik; Marinello, Carlo P; Sendi, Pedram

2012-10-01

Markov models are mathematical models that can be used to describe disease progression and evaluate the cost-effectiveness of medical interventions. Markov models allow projecting clinical and economic outcomes into the future and are therefore frequently used to estimate long-term outcomes of medical interventions. The purpose of this paper is to demonstrate its use in dentistry, using the example of resin-bonded bridges to replace missing teeth, and to review the literature. We used literature data and a four-state Markov model to project long-term outcomes of resin-bonded bridges over a time horizon of 60 years. In addition, the literature was searched in PubMed Medline for research articles on the application of Markov models in dentistry.

Decoding and modelling of time series count data using Poisson hidden Markov model and Markov ordinal logistic regression models.

PubMed

Sebastian, Tunny; Jeyaseelan, Visalakshi; Jeyaseelan, Lakshmanan; Anandan, Shalini; George, Sebastian; Bangdiwala, Shrikant I

2018-01-01

Hidden Markov models are stochastic models in which the observations are assumed to follow a mixture distribution, but the parameters of the components are governed by a Markov chain which is unobservable. The issues related to the estimation of Poisson-hidden Markov models in which the observations are coming from mixture of Poisson distributions and the parameters of the component Poisson distributions are governed by an m-state Markov chain with an unknown transition probability matrix are explained here. These methods were applied to the data on Vibrio cholerae counts reported every month for 11-year span at Christian Medical College, Vellore, India. Using Viterbi algorithm, the best estimate of the state sequence was obtained and hence the transition probability matrix. The mean passage time between the states were estimated. The 95% confidence interval for the mean passage time was estimated via Monte Carlo simulation. The three hidden states of the estimated Markov chain are labelled as 'Low', 'Moderate' and 'High' with the mean counts of 1.4, 6.6 and 20.2 and the estimated average duration of stay of 3, 3 and 4 months, respectively. Environmental risk factors were studied using Markov ordinal logistic regression analysis. No significant association was found between disease severity levels and climate components.

Caliber Corrected Markov Modeling (C2M2): Correcting Equilibrium Markov Models.

PubMed

Dixit, Purushottam D; Dill, Ken A

2018-02-13

Rate processes are often modeled using Markov State Models (MSMs). Suppose you know a prior MSM and then learn that your prediction of some particular observable rate is wrong. What is the best way to correct the whole MSM? For example, molecular dynamics simulations of protein folding may sample many microstates, possibly giving correct pathways through them while also giving the wrong overall folding rate when compared to experiment. Here, we describe Caliber Corrected Markov Modeling (C 2 M 2 ), an approach based on the principle of maximum entropy for updating a Markov model by imposing state- and trajectory-based constraints. We show that such corrections are equivalent to asserting position-dependent diffusion coefficients in continuous-time continuous-space Markov processes modeled by a Smoluchowski equation. We derive the functional form of the diffusion coefficient explicitly in terms of the trajectory-based constraints. We illustrate with examples of 2D particle diffusion and an overdamped harmonic oscillator.

Building Simple Hidden Markov Models. Classroom Notes

ERIC Educational Resources Information Center

Ching, Wai-Ki; Ng, Michael K.

2004-01-01

Hidden Markov models (HMMs) are widely used in bioinformatics, speech recognition and many other areas. This note presents HMMs via the framework of classical Markov chain models. A simple example is given to illustrate the model. An estimation method for the transition probabilities of the hidden states is also discussed.

Economic evaluation of long-term impacts of universal newborn hearing screening.

PubMed

Chiou, Shu-Ti; Lung, Hou-Ling; Chen, Li-Sheng; Yen, Amy Ming-Fang; Fann, Jean Ching-Yuan; Chiu, Sherry Yueh-Hsia; Chen, Hsiu-Hsi

2017-01-01

Little is known about the long-term efficacious and economic impacts of universal newborn hearing screening (UNHS). An analytical Markov decision model was framed with two screening strategies: UNHS with transient evoked otoacoustic emission (TEOAE) test and automatic acoustic brainstem response (aABR) test against no screening. By estimating intervention and long-term costs on treatment and productivity losses and the utility of life years determined by the status of hearing loss, we computed base-case estimates of the incremental cost-utility ratios (ICURs). The scattered plot of ICUR and acceptability curve was used to assess the economic results of aABR versus TEOAE or both versus no screening. A hypothetical cohort of 200,000 Taiwanese newborns. TEOAE and aABR dominated over no screening strategy (ICUR = $-4800.89 and $-4111.23, indicating less cost and more utility). Given $20,000 of willingness to pay (WTP), the probability of being cost-effective of aABR against TEOAE was up to 90%. UNHS for hearing loss with aABR is the most economic option and supported by economically evidence-based evaluation from societal perspective.

Classification of customer lifetime value models using Markov chain

NASA Astrophysics Data System (ADS)

Permana, Dony; Pasaribu, Udjianna S.; Indratno, Sapto W.; Suprayogi

2017-10-01

A firm’s potential reward in future time from a customer can be determined by customer lifetime value (CLV). There are some mathematic methods to calculate it. One method is using Markov chain stochastic model. Here, a customer is assumed through some states. Transition inter the states follow Markovian properties. If we are given some states for a customer and the relationships inter states, then we can make some Markov models to describe the properties of the customer. As Markov models, CLV is defined as a vector contains CLV for a customer in the first state. In this paper we make a classification of Markov Models to calculate CLV. Start from two states of customer model, we make develop in many states models. The development a model is based on weaknesses in previous model. Some last models can be expected to describe how real characters of customers in a firm.

Feasible economic strategies to improve screening compliance for colorectal cancer in Korea

PubMed Central

Park, Sang Min; Yun, Young Ho; Kwon, Soonman

2005-01-01

AIM: While colorectal cancer (CRC) is an ideal target for population screening, physician and patient attitudes contribute to low levels of screening uptake. This study was carried out to find feasible economic strategies to improve the CRC screening compliance in Korea. METHODS: The natural history of a simulated cohort of 50-year-old Korean in the general population was modeled with CRC screening until the age of 80 years. Cases of positive results were worked up with colonoscopy. After polypectomy, colonoscopy was repeated every 3 years. Baseline screening compliance without insurance coverage by the national health insurance (NHI) was assumed to be 30%. If NHI covered the CRC screening or the reimbursement of screening to physicians increased, the compliance was assumed to increase. We evaluated 16 different CRC screening strategies based on Markov model. RESULTS: When the NHI did not cover the screening and compliance was 30%, non-dominated strategies were colonoscopy every 5 years (COL5) and colonoscopy every 3 years (COL3). In all scenarios of various compliance rates with raised coverage of the NHI and increased reimbursement of colonoscopy, COL10, COL5 and COL3 were non-dominated strategies, and COL10 had lower or minimal incremental medical cost and financial burden on the NHI than the strategy of no screening. These results were stable with sensitivity analyses. CONCLUSION: Economic strategies for promoting screening compliance can be accompanied by expanding insurance coverage by the NHI and by increasing reimbursement for CRC screening to providers. COL10 was a cost-effective and cost saving screening strategy for CRC in Korea. PMID:15786532

Predicting the effectiveness of the Finnish population-based colorectal cancer screening programme.

PubMed

Chiu, Sherry Yueh-Hsia; Malila, Nea; Yen, Amy Ming-Fang; Chen, Sam Li-Sheng; Fann, Jean Ching-Yuan; Hakama, Matti

2017-12-01

Objective Because colorectal cancer (CRC) has a long natural history, estimating the effectiveness of CRC screening programmes requires long-term follow-up. As an alternative, we here demonstrate the use of a temporal multi-state natural history model to predict the effectiveness of CRC screening. Methods In the Finnish population-based biennial CRC screening programme using faecal occult blood tests (FOBT), which was conducted in a randomised health services study, we estimated the pre-clinical incidence, the mean sojourn time (MST), and the sensitivity of FOBT using a Markov model to analyse data from 2004 to 2007. These estimates were applied to predict, through simulation, the effects of five rounds of screening on the relative rate of reducing advanced CRC with 6 years of follow-up, and on the reduction in mortality with 10 years of follow-up, in a cohort of 500,000 subjects aged 60 to 69. Results For localised and non-localised CRC, respectively, the MST was 2.06 and 1.36 years and the sensitivity estimates were 65.12% and 73.70%. The predicted relative risk of non-localised CRC and death from CRC in the screened compared with the control population was 0.86 (95% CI: 0.79-0.98) and 0.91 (95% CI: 0.85-1.02), respectively. Conclusion Based on the preliminary results of the Finnish CRC screening programme, our model predicted a 9% reduction in CRC mortality and a 14% reduction in advanced CRC.

Cost-effectiveness of Colorectal Cancer Screening and Treatment Methods: Mapping of Systematic Reviews.

PubMed

Abdolahi, Hossein Mashhadi; Asiabar, Ali Sarabi; Azami-Aghdash, Saber; Pournaghi-Azar, Fatemeh; Rezapour, Aziz

2018-01-01

Due to extensive literature on colorectal cancer and their heterogeneous results, this study aimed to summarize the systematic reviews which review the cost-effectiveness studies on different aspects of colorectal cancer. The required data were collected by searching the following key words according to MeSH: "colorectal cancer," "colorectal oncology," "colorectal carcinoma," "colorectal neoplasm," "colorectal tumors," "cost-effectiveness," "systematic review," and "meta-analysis." The following databases were searched: PubMed, Cochrane, Google Scholar, and Scopus. Two reviewers evaluated the articles according to the checklist of "assessment of multiple systematic reviews" (AMSTAR) tool. Finally, eight systematic reviews were included in the study. The Drummond checklist was mostly used for assessing the quality of the articles. The main perspective was related to the payer and the least was relevant to the social. The majority of the cases referred to sensitivity analysis (in 76% of the cases) and the lowest point also was allocated to discounting (in 37% of cases). The Markov model was used most widely in the studies. Treatment methods examined in the studies were not cost-effective in comparison with the studied units. Among the screening methods, computerized tomographic colonography and fecal DNA were cost-effective. The average score of the articles' qualities was high (9.8 out of 11). The community perspective should be taken into consideration at large in the studies. It is necessary to pay more attention to discounting subject in studies. More frequent application of the Markov model is recommended.

Cost-Effectiveness Analysis of Screening for and Managing Identified Hypertension for Cardiovascular Disease Prevention in Vietnam

PubMed Central

Nguyen, Thi-Phuong-Lan; Wright, E. Pamela; Nguyen, Thanh-Trung; Schuiling-Veninga, C. C. M.; Bijlsma, M. J.; Nguyen, Thi-Bach-Yen; Postma, M. J.

2016-01-01

Objective To inform development of guidelines for hypertension management in Vietnam, we evaluated the cost-effectiveness of different strategies on screening for hypertension in preventing cardiovascular disease (CVD). Methods A decision tree was combined with a Markov model to measure incremental cost-effectiveness of different approaches to hypertension screening. Values used as input parameters for the model were taken from different sources. Various screening intervals (one-off, annually, biannually) and starting ages to screen (35, 45 or 55 years) and coverage of treatment were analysed. We ran both a ten-year and a lifetime horizon. Input parameters for the models were extracted from local and regional data. Probabilistic sensitivity analysis was used to evaluate parameter uncertainty. A threshold of three times GDP per capita was applied. Results Cost per quality adjusted life year (QALY) gained varied in different screening scenarios. In a ten-year horizon, the cost-effectiveness of screening for hypertension ranged from cost saving to Int$ 758,695 per QALY gained. For screening of men starting at 55 years, all screening scenarios gave a high probability of being cost-effective. For screening of females starting at 55 years, the probability of favourable cost-effectiveness was 90% with one-off screening. In a lifetime horizon, cost per QALY gained was lower than the threshold of Int$ 15,883 in all screening scenarios among males. Similar results were found in females when starting screening at 55 years. Starting screening in females at 45 years had a high probability of being cost-effective if screening biannually was combined with increasing coverage of treatment by 20% or even if sole biannual screening was considered. Conclusion From a health economic perspective, integrating screening for hypertension into routine medical examination and related coverage by health insurance could be recommended. Screening for hypertension has a high probability of being cost-effective in preventing CVD. An adequate screening strategy can best be selected based on age, sex and screening interval. PMID:27192051

Cost-effectiveness and population outcomes of general population screening for hepatitis C.

PubMed

Coffin, Phillip O; Scott, John D; Golden, Matthew R; Sullivan, Sean D

2012-05-01

Current US guidelines recommend limiting hepatitis C virus (HCV) screening to high-risk individuals, and 50%-75% of infected persons remain unaware of their status. To estimate the cost-effectiveness and population-level impact of adding one-time HCV screening of US population aged 20-69 years to current guidelines, we developed a decision analytic model for the screening intervention and Markov model with annual transitions to estimate natural history. Subanalyses included protease inhibitor therapy and screening those at highest risk of infection (birth year 1945-1965). We relied on published literature and took a lifetime, societal perspective. Compared to current guidelines, incremental cost per quality-adjusted life year gained (ICER) was $7900 for general population screening and $4200 for screening by birth year, which dominated general population screening if cost, clinician uptake, and median age of diagnoses were assumed equivalent. General population screening remained cost-effective in all one-way sensitivity analyses, 30 000 Monte Carlo simulations, and scenarios in which background mortality was doubled, all genotype 1 patients were treated with protease inhibitors, and most parameters were set unfavorable to increased screening. ICER was lowest if screening was applied to a population with liver fibrosis similar to 2010 estimates. Approximately 1% of liver-related deaths would be averted per 15% of the general population screened; the impact would be greater with improved referral, treatment uptake, and cure. Broader screening for HCV would likely be cost-effective, but significantly reducing HCV-related morbidity and mortality would also require improved rates of referral, treatment, and cure.

Driving style recognition method using braking characteristics based on hidden Markov model

PubMed Central

Wu, Chaozhong; Lyu, Nengchao; Huang, Zhen

2017-01-01

Since the advantage of hidden Markov model in dealing with time series data and for the sake of identifying driving style, three driving style (aggressive, moderate and mild) are modeled reasonably through hidden Markov model based on driver braking characteristics to achieve efficient driving style. Firstly, braking impulse and the maximum braking unit area of vacuum booster within a certain time are collected from braking operation, and then general braking and emergency braking characteristics are extracted to code the braking characteristics. Secondly, the braking behavior observation sequence is used to describe the initial parameters of hidden Markov model, and the generation of the hidden Markov model for differentiating and an observation sequence which is trained and judged by the driving style is introduced. Thirdly, the maximum likelihood logarithm could be implied from the observable parameters. The recognition accuracy of algorithm is verified through experiments and two common pattern recognition algorithms. The results showed that the driving style discrimination based on hidden Markov model algorithm could realize effective discriminant of driving style. PMID:28837580

Observation uncertainty in reversible Markov chains.

PubMed

Metzner, Philipp; Weber, Marcus; Schütte, Christof

2010-09-01

In many applications one is interested in finding a simplified model which captures the essential dynamical behavior of a real life process. If the essential dynamics can be assumed to be (approximately) memoryless then a reasonable choice for a model is a Markov model whose parameters are estimated by means of Bayesian inference from an observed time series. We propose an efficient Monte Carlo Markov chain framework to assess the uncertainty of the Markov model and related observables. The derived Gibbs sampler allows for sampling distributions of transition matrices subject to reversibility and/or sparsity constraints. The performance of the suggested sampling scheme is demonstrated and discussed for a variety of model examples. The uncertainty analysis of functions of the Markov model under investigation is discussed in application to the identification of conformations of the trialanine molecule via Robust Perron Cluster Analysis (PCCA+) .

Phasic Triplet Markov Chains.

PubMed

El Yazid Boudaren, Mohamed; Monfrini, Emmanuel; Pieczynski, Wojciech; Aïssani, Amar

2014-11-01

Hidden Markov chains have been shown to be inadequate for data modeling under some complex conditions. In this work, we address the problem of statistical modeling of phenomena involving two heterogeneous system states. Such phenomena may arise in biology or communications, among other fields. Namely, we consider that a sequence of meaningful words is to be searched within a whole observation that also contains arbitrary one-by-one symbols. Moreover, a word may be interrupted at some site to be carried on later. Applying plain hidden Markov chains to such data, while ignoring their specificity, yields unsatisfactory results. The Phasic triplet Markov chain, proposed in this paper, overcomes this difficulty by means of an auxiliary underlying process in accordance with the triplet Markov chains theory. Related Bayesian restoration techniques and parameters estimation procedures according to the new model are then described. Finally, to assess the performance of the proposed model against the conventional hidden Markov chain model, experiments are conducted on synthetic and real data.

[Evaluation of the cost-effectiveness of two alternative human papillomavirus vaccines as prophylaxis against uterine cervical cancer].

PubMed

Bolaños-Díaz, Rafael; Tejada, Romina A; Beltrán, Jessica; Escobedo-Palza, Seimer

2016-01-01

To determine the cost-effectiveness of human papillomavirus (HPV) vaccination and cervical lesion screening versus screening alone for the prevention of uterine cervical cancer (UCC). This cost-effectiveness evaluation from the perspective of the Ministry of Health employed a Markov model with a 70-year time horizon and three alternatives for UCC prevention (screening alone, screening + bivalent vaccine, and screening + quadrivalent vaccine) in a hypothetical cohort of 10-year-old girls. Our model, which was particularly sensitive to variations in coverage and in the prevalence of persistent infection by oncologic genotypes not included in the vaccine, revealed that HPV vaccination and screening is more cost-effective than screening alone, assuming a payment availability from S/ 2 000 (US dollars (USD) 1 290.32) per subject. In the deterministic analysis, the bivalent vaccine was marginally more cost-effective than the quadrivalent vaccine (S/ 48 [USD 30.97] vs. S/ 166 [USD 107.10] per quality-adjusted life-year, respectively). However, in the probabilistic analysis, both interventions generated clouds of overlapping points and were thus cost-effective and interchangeable, although the quadrivalent vaccine tended to be more cost-effective. Assuming a payment availability from S/ 2000 [USD 1,290.32], screening and vaccination were more cost-effective than screening alone. The difference in cost-effectiveness between the two vaccines lacked probabilistic robustness, and therefore the vaccines can be considered interchangeable from a cost-effectiveness perspective.

Recommendations for Methicillin-Resistant Staphylococcus aureus Prevention in Adult ICUs: A Cost-Effectiveness Analysis.

PubMed

Whittington, Melanie D; Atherly, Adam J; Curtis, Donna J; Lindrooth, Richard C; Bradley, Cathy J; Campbell, Jonathan D

2017-08-01

Patients in the ICU are at the greatest risk of contracting healthcare-associated infections like methicillin-resistant Staphylococcus aureus. This study calculates the cost-effectiveness of methicillin-resistant S aureus prevention strategies and recommends specific strategies based on screening test implementation. A cost-effectiveness analysis using a Markov model from the hospital perspective was conducted to determine if the implementation costs of methicillin-resistant S aureus prevention strategies are justified by associated reductions in methicillin-resistant S aureus infections and improvements in quality-adjusted life years. Univariate and probabilistic sensitivity analyses determined the influence of input variation on the cost-effectiveness. ICU. Hypothetical cohort of adults admitted to the ICU. Three prevention strategies were evaluated, including universal decolonization, targeted decolonization, and screening and isolation. Because prevention strategies have a screening component, the screening test in the model was varied to reflect commonly used screening test categories, including conventional culture, chromogenic agar, and polymerase chain reaction. Universal and targeted decolonization are less costly and more effective than screening and isolation. This is consistent for all screening tests. When compared with targeted decolonization, universal decolonization is cost-saving to cost-effective, with maximum cost savings occurring when a hospital uses more expensive screening tests like polymerase chain reaction. Results were robust to sensitivity analyses. As compared with screening and isolation, the current standard practice in ICUs, targeted decolonization, and universal decolonization are less costly and more effective. This supports updating the standard practice to a decolonization approach.

A Cost-Utility Analysis of Prostate Cancer Screening in Australia.

PubMed

Keller, Andrew; Gericke, Christian; Whitty, Jennifer A; Yaxley, John; Kua, Boon; Coughlin, Geoff; Gianduzzo, Troy

2017-02-01

The Göteborg randomised population-based prostate cancer screening trial demonstrated that prostate-specific antigen (PSA)-based screening reduces prostate cancer deaths compared with an age-matched control group. Utilising the prostate cancer detection rates from this study, we investigated the clinical and cost effectiveness of a similar PSA-based screening strategy for an Australian population of men aged 50-69 years. A decision model that incorporated Markov processes was developed from a health system perspective. The base-case scenario compared a population-based screening programme with current opportunistic screening practices. Costs, utility values, treatment patterns and background mortality rates were derived from Australian data. All costs were adjusted to reflect July 2015 Australian dollars (A$). An alternative scenario compared systematic with opportunistic screening but with optimisation of active surveillance (AS) uptake in both groups. A discount rate of 5 % for costs and benefits was utilised. Univariate and probabilistic sensitivity analyses were performed to assess the effect of variable uncertainty on model outcomes. Our model very closely replicated the number of deaths from both prostate cancer and background mortality in the Göteborg study. The incremental cost per quality-adjusted life-year (QALY) for PSA screening was A$147,528. However, for years of life gained (LYGs), PSA-based screening (A$45,890/LYG) appeared more favourable. Our alternative scenario with optimised AS improved cost utility to A$45,881/QALY, with screening becoming cost effective at a 92 % AS uptake rate. Both modelled scenarios were most sensitive to the utility of patients before and after intervention, and the discount rate used. PSA-based screening is not cost effective compared with Australia's assumed willingness-to-pay threshold of A$50,000/QALY. It appears more cost effective if LYGs are used as the relevant outcome, and is more cost effective than the established Australian breast cancer screening programme on this basis. Optimised utilisation of AS increases the cost effectiveness of prostate cancer screening dramatically.

Educational Aspirations: Markov and Poisson Models. Rural Industrial Development Project Working Paper Number 14, August 1971.

ERIC Educational Resources Information Center

Kayser, Brian D.

The fit of educational aspirations of Illinois rural high school youths to 3 related one-parameter mathematical models was investigated. The models used were the continuous-time Markov chain model, the discrete-time Markov chain, and the Poisson distribution. The sample of 635 students responded to questionnaires from 1966 to 1969 as part of an…

A stochastic model for tumor geometry evolution during radiation therapy in cervical cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Yifang; Lee, Chi-Guhn; Chan, Timothy C. Y., E-mail: tcychan@mie.utoronto.ca

2014-02-15

Purpose: To develop mathematical models to predict the evolution of tumor geometry in cervical cancer undergoing radiation therapy. Methods: The authors develop two mathematical models to estimate tumor geometry change: a Markov model and an isomorphic shrinkage model. The Markov model describes tumor evolution by investigating the change in state (either tumor or nontumor) of voxels on the tumor surface. It assumes that the evolution follows a Markov process. Transition probabilities are obtained using maximum likelihood estimation and depend on the states of neighboring voxels. The isomorphic shrinkage model describes tumor shrinkage or growth in terms of layers of voxelsmore » on the tumor surface, instead of modeling individual voxels. The two proposed models were applied to data from 29 cervical cancer patients treated at Princess Margaret Cancer Centre and then compared to a constant volume approach. Model performance was measured using sensitivity and specificity. Results: The Markov model outperformed both the isomorphic shrinkage and constant volume models in terms of the trade-off between sensitivity (target coverage) and specificity (normal tissue sparing). Generally, the Markov model achieved a few percentage points in improvement in either sensitivity or specificity compared to the other models. The isomorphic shrinkage model was comparable to the Markov approach under certain parameter settings. Convex tumor shapes were easier to predict. Conclusions: By modeling tumor geometry change at the voxel level using a probabilistic model, improvements in target coverage and normal tissue sparing are possible. Our Markov model is flexible and has tunable parameters to adjust model performance to meet a range of criteria. Such a model may support the development of an adaptive paradigm for radiation therapy of cervical cancer.« less

Refining value-at-risk estimates using a Bayesian Markov-switching GJR-GARCH copula-EVT model.

PubMed

Sampid, Marius Galabe; Hasim, Haslifah M; Dai, Hongsheng

2018-01-01

In this paper, we propose a model for forecasting Value-at-Risk (VaR) using a Bayesian Markov-switching GJR-GARCH(1,1) model with skewed Student's-t innovation, copula functions and extreme value theory. A Bayesian Markov-switching GJR-GARCH(1,1) model that identifies non-constant volatility over time and allows the GARCH parameters to vary over time following a Markov process, is combined with copula functions and EVT to formulate the Bayesian Markov-switching GJR-GARCH(1,1) copula-EVT VaR model, which is then used to forecast the level of risk on financial asset returns. We further propose a new method for threshold selection in EVT analysis, which we term the hybrid method. Empirical and back-testing results show that the proposed VaR models capture VaR reasonably well in periods of calm and in periods of crisis.

Indexed semi-Markov process for wind speed modeling.

NASA Astrophysics Data System (ADS)

Petroni, F.; D'Amico, G.; Prattico, F.

2012-04-01

The increasing interest in renewable energy leads scientific research to find a better way to recover most of the available energy. Particularly, the maximum energy recoverable from wind is equal to 59.3% of that available (Betz law) at a specific pitch angle and when the ratio between the wind speed in output and in input is equal to 1/3. The pitch angle is the angle formed between the airfoil of the blade of the wind turbine and the wind direction. Old turbine and a lot of that actually marketed, in fact, have always the same invariant geometry of the airfoil. This causes that wind turbines will work with an efficiency that is lower than 59.3%. New generation wind turbines, instead, have a system to variate the pitch angle by rotating the blades. This system able the wind turbines to recover, at different wind speed, always the maximum energy, working in Betz limit at different speed ratios. A powerful system control of the pitch angle allows the wind turbine to recover better the energy in transient regime. A good stochastic model for wind speed is then needed to help both the optimization of turbine design and to assist the system control to predict the value of the wind speed to positioning the blades quickly and correctly. The possibility to have synthetic data of wind speed is a powerful instrument to assist designer to verify the structures of the wind turbines or to estimate the energy recoverable from a specific site. To generate synthetic data, Markov chains of first or higher order are often used [1,2,3]. In particular in [1] is presented a comparison between a first-order Markov chain and a second-order Markov chain. A similar work, but only for the first-order Markov chain, is conduced by [2], presenting the probability transition matrix and comparing the energy spectral density and autocorrelation of real and synthetic wind speed data. A tentative to modeling and to join speed and direction of wind is presented in [3], by using two models, first-order Markov chain with different number of states, and Weibull distribution. All this model use Markov chains to generate synthetic wind speed time series but the search for a better model is still open. Approaching this issue, we applied new models which are generalization of Markov models. More precisely we applied semi-Markov models to generate synthetic wind speed time series. In a previous work we proposed different semi-Markov models, showing their ability to reproduce the autocorrelation structures of wind speed data. In that paper we showed also that the autocorrelation is higher with respect to the Markov model. Unfortunately this autocorrelation was still too small compared to the empirical one. In order to overcome the problem of low autocorrelation, in this paper we propose an indexed semi-Markov model. More precisely we assume that wind speed is described by a discrete time homogeneous semi-Markov process. We introduce a memory index which takes into account the periods of different wind activities. With this model the statistical characteristics of wind speed are faithfully reproduced. The wind is a very unstable phenomenon characterized by a sequence of lulls and sustained speeds, and a good wind generator must be able to reproduce such sequences. To check the validity of the predictive semi-Markovian model, the persistence of synthetic winds were calculated, then averaged and computed. The model is used to generate synthetic time series for wind speed by means of Monte Carlo simulations and the time lagged autocorrelation is used to compare statistical properties of the proposed models with those of real data and also with a time series generated though a simple Markov chain. [1] A. Shamshad, M.A. Bawadi, W.M.W. Wan Hussin, T.A. Majid, S.A.M. Sanusi, First and second order Markov chain models for synthetic generation of wind speed time series, Energy 30 (2005) 693-708. [2] H. Nfaoui, H. Essiarab, A.A.M. Sayigh, A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco, Renewable Energy 29 (2004) 1407-1418. [3] F. Youcef Ettoumi, H. Sauvageot, A.-E.-H. Adane, Statistical bivariate modeling of wind using first-order Markov chain and Weibull distribution, Renewable Energy 28 (2003) 1787-1802.

Cost effectiveness of screening immigrants for hepatitis B.

PubMed

Wong, William W L; Woo, Gloria; Jenny Heathcote, E; Krahn, Murray

2011-09-01

The prevalence of chronic hepatitis B (CHB) infection among the immigrants of North America ranges from 2 to 15%, among whom 40% develop advanced liver disease. Screening for hepatitis B surface antigen is not recommended for immigrants. The objective of this study is to estimate the health and economic effects of screening strategies for CHB among immigrants. We used the Markov model to examine the cost-effectiveness of three screening strategies: (i) 'No screening'; (ii) 'Screen and Treat' and (iii) 'Screen, Treat and Vaccinate' for 20-65 years old individuals who were born abroad but are currently living in Canada. Model data were obtained from the published literature. We measured predicted hepatitis B virus (HBV)-related deaths, costs (2008 Canadian Dollars), quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio (ICER). Our results show that screening all immigrants will prevent 59 HBV-related deaths per 10, 000 persons screened over the lifetime of the cohort. Screening was associated with an increase in quality-adjusted life expectancy (0.024 QALYs) and cost ($1665) per person with an ICER of $69, 209/QALY gained compared with 'No screening'. The 'Screen, Treat and Vaccinate' costs an additional $81, generates an additional 0.000022 QALYs per person, with an ICER of $3, 648,123/QALY compared with the 'Screen and Treat'. Sensitivity analyses suggested that the 'Screen and Treat' is likely to be moderately cost-effective. We show that a selective hepatitis B screening programme targeted at all immigrants in Canada is likely to be moderately cost-effective. Identification of silent CHB infection with the offer of treatment when appropriate can extend the lives of immigrants at reasonable cost. © 2011 John Wiley & Sons A/S.

Markov models of genome segmentation

NASA Astrophysics Data System (ADS)

Thakur, Vivek; Azad, Rajeev K.; Ramaswamy, Ram

2007-01-01

We introduce Markov models for segmentation of symbolic sequences, extending a segmentation procedure based on the Jensen-Shannon divergence that has been introduced earlier. Higher-order Markov models are more sensitive to the details of local patterns and in application to genome analysis, this makes it possible to segment a sequence at positions that are biologically meaningful. We show the advantage of higher-order Markov-model-based segmentation procedures in detecting compositional inhomogeneity in chimeric DNA sequences constructed from genomes of diverse species, and in application to the E. coli K12 genome, boundaries of genomic islands, cryptic prophages, and horizontally acquired regions are accurately identified.

Modeling haplotype block variation using Markov chains.

PubMed

Greenspan, G; Geiger, D

2006-04-01

Models of background variation in genomic regions form the basis of linkage disequilibrium mapping methods. In this work we analyze a background model that groups SNPs into haplotype blocks and represents the dependencies between blocks by a Markov chain. We develop an error measure to compare the performance of this model against the common model that assumes that blocks are independent. By examining data from the International Haplotype Mapping project, we show how the Markov model over haplotype blocks is most accurate when representing blocks in strong linkage disequilibrium. This contrasts with the independent model, which is rendered less accurate by linkage disequilibrium. We provide a theoretical explanation for this surprising property of the Markov model and relate its behavior to allele diversity.

Modeling Haplotype Block Variation Using Markov Chains

PubMed Central

Greenspan, G.; Geiger, D.

2006-01-01

Models of background variation in genomic regions form the basis of linkage disequilibrium mapping methods. In this work we analyze a background model that groups SNPs into haplotype blocks and represents the dependencies between blocks by a Markov chain. We develop an error measure to compare the performance of this model against the common model that assumes that blocks are independent. By examining data from the International Haplotype Mapping project, we show how the Markov model over haplotype blocks is most accurate when representing blocks in strong linkage disequilibrium. This contrasts with the independent model, which is rendered less accurate by linkage disequilibrium. We provide a theoretical explanation for this surprising property of the Markov model and relate its behavior to allele diversity. PMID:16361244

Modeling the coupled return-spread high frequency dynamics of large tick assets

NASA Astrophysics Data System (ADS)

Curato, Gianbiagio; Lillo, Fabrizio

2015-01-01

Large tick assets, i.e. assets where one tick movement is a significant fraction of the price and bid-ask spread is almost always equal to one tick, display a dynamics in which price changes and spread are strongly coupled. We present an approach based on the hidden Markov model, also known in econometrics as the Markov switching model, for the dynamics of price changes, where the latent Markov process is described by the transitions between spreads. We then use a finite Markov mixture of logit regressions on past squared price changes to describe temporal dependencies in the dynamics of price changes. The model can thus be seen as a double chain Markov model. We show that the model describes the shape of the price change distribution at different time scales, volatility clustering, and the anomalous decrease of kurtosis. We calibrate our models based on Nasdaq stocks and we show that this model reproduces remarkably well the statistical properties of real data.

Markov-modulated Markov chains and the covarion process of molecular evolution.

PubMed

Galtier, N; Jean-Marie, A

2004-01-01

The covarion (or site specific rate variation, SSRV) process of biological sequence evolution is a process by which the evolutionary rate of a nucleotide/amino acid/codon position can change in time. In this paper, we introduce time-continuous, space-discrete, Markov-modulated Markov chains as a model for representing SSRV processes, generalizing existing theory to any model of rate change. We propose a fast algorithm for diagonalizing the generator matrix of relevant Markov-modulated Markov processes. This algorithm makes phylogeny likelihood calculation tractable even for a large number of rate classes and a large number of states, so that SSRV models become applicable to amino acid or codon sequence datasets. Using this algorithm, we investigate the accuracy of the discrete approximation to the Gamma distribution of evolutionary rates, widely used in molecular phylogeny. We show that a relatively large number of classes is required to achieve accurate approximation of the exact likelihood when the number of analyzed sequences exceeds 20, both under the SSRV and among site rate variation (ASRV) models.

Fast-slow asymptotics for a Markov chain model of fast sodium current

NASA Astrophysics Data System (ADS)

Starý, Tomáš; Biktashev, Vadim N.

2017-09-01

We explore the feasibility of using fast-slow asymptotics to eliminate the computational stiffness of discrete-state, continuous-time deterministic Markov chain models of ionic channels underlying cardiac excitability. We focus on a Markov chain model of fast sodium current, and investigate its asymptotic behaviour with respect to small parameters identified in different ways.

Behavioral Analysis of Visitors to a Medical Institution's Website Using Markov Chain Monte Carlo Methods.

PubMed

Suzuki, Teppei; Tani, Yuji; Ogasawara, Katsuhiko

2016-07-25

Consistent with the "attention, interest, desire, memory, action" (AIDMA) model of consumer behavior, patients collect information about available medical institutions using the Internet to select information for their particular needs. Studies of consumer behavior may be found in areas other than medical institution websites. Such research uses Web access logs for visitor search behavior. At this time, research applying the patient searching behavior model to medical institution website visitors is lacking. We have developed a hospital website search behavior model using a Bayesian approach to clarify the behavior of medical institution website visitors and determine the probability of their visits, classified by search keyword. We used the website data access log of a clinic of internal medicine and gastroenterology in the Sapporo suburbs, collecting data from January 1 through June 31, 2011. The contents of the 6 website pages included the following: home, news, content introduction for medical examinations, mammography screening, holiday person-on-duty information, and other. The search keywords we identified as best expressing website visitor needs were listed as the top 4 headings from the access log: clinic name, clinic name + regional name, clinic name + medical examination, and mammography screening. Using the search keywords as the explaining variable, we built a binomial probit model that allows inspection of the contents of each purpose variable. Using this model, we determined a beta value and generated a posterior distribution. We performed the simulation using Markov Chain Monte Carlo methods with a noninformation prior distribution for this model and determined the visit probability classified by keyword for each category. In the case of the keyword "clinic name," the visit probability to the website, repeated visit to the website, and contents page for medical examination was positive. In the case of the keyword "clinic name and regional name," the probability for a repeated visit to the website and the mammography screening page was negative. In the case of the keyword "clinic name + medical examination," the visit probability to the website was positive, and the visit probability to the information page was negative. When visitors referred to the keywords "mammography screening," the visit probability to the mammography screening page was positive (95% highest posterior density interval = 3.38-26.66). Further analysis for not only the clinic website but also various other medical institution websites is necessary to build a general inspection model for medical institution websites; we want to consider this in future research. Additionally, we hope to use the results obtained in this study as a prior distribution for future work to conduct higher-precision analysis.

Behavioral Analysis of Visitors to a Medical Institution’s Website Using Markov Chain Monte Carlo Methods

PubMed Central

Tani, Yuji

2016-01-01

Background Consistent with the “attention, interest, desire, memory, action” (AIDMA) model of consumer behavior, patients collect information about available medical institutions using the Internet to select information for their particular needs. Studies of consumer behavior may be found in areas other than medical institution websites. Such research uses Web access logs for visitor search behavior. At this time, research applying the patient searching behavior model to medical institution website visitors is lacking. Objective We have developed a hospital website search behavior model using a Bayesian approach to clarify the behavior of medical institution website visitors and determine the probability of their visits, classified by search keyword. Methods We used the website data access log of a clinic of internal medicine and gastroenterology in the Sapporo suburbs, collecting data from January 1 through June 31, 2011. The contents of the 6 website pages included the following: home, news, content introduction for medical examinations, mammography screening, holiday person-on-duty information, and other. The search keywords we identified as best expressing website visitor needs were listed as the top 4 headings from the access log: clinic name, clinic name + regional name, clinic name + medical examination, and mammography screening. Using the search keywords as the explaining variable, we built a binomial probit model that allows inspection of the contents of each purpose variable. Using this model, we determined a beta value and generated a posterior distribution. We performed the simulation using Markov Chain Monte Carlo methods with a noninformation prior distribution for this model and determined the visit probability classified by keyword for each category. Results In the case of the keyword “clinic name,” the visit probability to the website, repeated visit to the website, and contents page for medical examination was positive. In the case of the keyword “clinic name and regional name,” the probability for a repeated visit to the website and the mammography screening page was negative. In the case of the keyword “clinic name + medical examination,” the visit probability to the website was positive, and the visit probability to the information page was negative. When visitors referred to the keywords “mammography screening,” the visit probability to the mammography screening page was positive (95% highest posterior density interval = 3.38-26.66). Conclusions Further analysis for not only the clinic website but also various other medical institution websites is necessary to build a general inspection model for medical institution websites; we want to consider this in future research. Additionally, we hope to use the results obtained in this study as a prior distribution for future work to conduct higher-precision analysis. PMID:27457537

Cost-effectiveness and budget impact analysis of a population-based screening program for colorectal cancer.

PubMed

Pil, L; Fobelets, M; Putman, K; Trybou, J; Annemans, L

2016-07-01

Colorectal cancer (CRC) is one of the leading causes of cancer mortality in Belgium. In Flanders (Belgium), a population-based screening program with a biennial immunochemical faecal occult blood test (iFOBT) in women and men aged 56-74 has been organised since 2013. This study assessed the cost-effectiveness and budget impact of the colorectal population-based screening program in Flanders (Belgium). A health economic model was conducted, consisting of a decision tree simulating the screening process and a Markov model, with a time horizon of 20years, simulating natural progression. Predicted mortality and incidence, total costs, and quality-adjusted life-years (QALYs) with and without the screening program were calculated in order to determine the incremental cost-effectiveness ratio of CRC screening. Deterministic and probabilistic sensitivity analyses were conducted, taking into account uncertainty of the model parameters. Mortality and incidence were predicted to decrease over 20years. The colorectal screening program in Flanders is found to be cost-effective with an ICER of 1681/QALY (95% CI -1317 to 6601) in males and €4,484/QALY (95% CI -3254 to 18,163). The probability of being cost-effective given a threshold of €35,000/QALY was 100% and 97.3%, respectively. The budget impact analysis showed the extra cost for the health care payer to be limited. This health economic analysis has shown that despite the possible adverse effects of screening and the extra costs for the health care payer and the patient, the population-based screening program for CRC in Flanders is cost-effective and should therefore be maintained. Copyright © 2016 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.

Screening for chronic kidney disease in Canadian indigenous peoples is cost-effective.

PubMed

Ferguson, Thomas W; Tangri, Navdeep; Tan, Zhi; James, Matthew T; Lavallee, Barry D A; Chartrand, Caroline D; McLeod, Lorraine L; Dart, Allison B; Rigatto, Claudio; Komenda, Paul V J

2017-07-01

Canadian indigenous (First Nations) have rates of kidney failure that are 2- to 4-fold higher than the non-indigenous general Canadian population. As such, a strategy of targeted screening and treatment for CKD may be cost-effective in this population. Our objective was to assess the cost utility of screening and subsequent treatment for CKD in rural Canadian indigenous adults by both estimated glomerular filtration rate and the urine albumin-to-creatinine ratio. A decision analytic Markov model was constructed comparing the screening and treatment strategy to usual care. Primary outcomes were presented as incremental cost-effectiveness ratios (ICERs) presented as a cost per quality-adjusted life-year (QALY). Screening for CKD was associated with an ICER of $23,700/QALY in comparison to usual care. Restricting the model to screening in communities accessed only by air travel (CKD prevalence 34.4%), this ratio fell to $7,790/QALY. In road accessible communities (CKD prevalence 17.6%) the ICER was $52,480/QALY. The model was robust to changes in influential variables when tested in univariate sensitivity analyses. Probabilistic sensitivity analysis found 72% of simulations to be cost-effective at a $50,000/QALY threshold and 93% of simulations to be cost-effective at a $100,000/QALY threshold. Thus, targeted screening and treatment for CKD using point-of-care testing equipment in rural Canadian indigenous populations is cost-effective, particularly in remote air access-only communities with the highest risk of CKD and kidney failure. Evaluation of targeted screening initiatives with cluster randomized controlled trials and integration of screening into routine clinical visits in communities with the highest risk is recommended. Copyright © 2017 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.

Revisiting Temporal Markov Chains for Continuum modeling of Transport in Porous Media

NASA Astrophysics Data System (ADS)

Delgoshaie, A. H.; Jenny, P.; Tchelepi, H.

2017-12-01

The transport of fluids in porous media is dominated by flow­-field heterogeneity resulting from the underlying permeability field. Due to the high uncertainty in the permeability field, many realizations of the reference geological model are used to describe the statistics of the transport phenomena in a Monte Carlo (MC) framework. There has been strong interest in working with stochastic formulations of the transport that are different from the standard MC approach. Several stochastic models based on a velocity process for tracer particle trajectories have been proposed. Previous studies have shown that for high variances of the log-conductivity, the stochastic models need to account for correlations between consecutive velocity transitions to predict dispersion accurately. The correlated velocity models proposed in the literature can be divided into two general classes of temporal and spatial Markov models. Temporal Markov models have been applied successfully to tracer transport in both the longitudinal and transverse directions. These temporal models are Stochastic Differential Equations (SDEs) with very specific drift and diffusion terms tailored for a specific permeability correlation structure. The drift and diffusion functions devised for a certain setup would not necessarily be suitable for a different scenario, (e.g., a different permeability correlation structure). The spatial Markov models are simple discrete Markov chains that do not require case specific assumptions. However, transverse spreading of contaminant plumes has not been successfully modeled with the available correlated spatial models. Here, we propose a temporal discrete Markov chain to model both the longitudinal and transverse dispersion in a two-dimensional domain. We demonstrate that these temporal Markov models are valid for different correlation structures without modification. Similar to the temporal SDEs, the proposed model respects the limited asymptotic transverse spreading of the plume in two-dimensional problems.

Irreversible Local Markov Chains with Rapid Convergence towards Equilibrium.

PubMed

Kapfer, Sebastian C; Krauth, Werner

2017-12-15

We study the continuous one-dimensional hard-sphere model and present irreversible local Markov chains that mix on faster time scales than the reversible heat bath or Metropolis algorithms. The mixing time scales appear to fall into two distinct universality classes, both faster than for reversible local Markov chains. The event-chain algorithm, the infinitesimal limit of one of these Markov chains, belongs to the class presenting the fastest decay. For the lattice-gas limit of the hard-sphere model, reversible local Markov chains correspond to the symmetric simple exclusion process (SEP) with periodic boundary conditions. The two universality classes for irreversible Markov chains are realized by the totally asymmetric SEP (TASEP), and by a faster variant (lifted TASEP) that we propose here. We discuss how our irreversible hard-sphere Markov chains generalize to arbitrary repulsive pair interactions and carry over to higher dimensions through the concept of lifted Markov chains and the recently introduced factorized Metropolis acceptance rule.

Irreversible Local Markov Chains with Rapid Convergence towards Equilibrium

NASA Astrophysics Data System (ADS)

Kapfer, Sebastian C.; Krauth, Werner

2017-12-01

We study the continuous one-dimensional hard-sphere model and present irreversible local Markov chains that mix on faster time scales than the reversible heat bath or Metropolis algorithms. The mixing time scales appear to fall into two distinct universality classes, both faster than for reversible local Markov chains. The event-chain algorithm, the infinitesimal limit of one of these Markov chains, belongs to the class presenting the fastest decay. For the lattice-gas limit of the hard-sphere model, reversible local Markov chains correspond to the symmetric simple exclusion process (SEP) with periodic boundary conditions. The two universality classes for irreversible Markov chains are realized by the totally asymmetric SEP (TASEP), and by a faster variant (lifted TASEP) that we propose here. We discuss how our irreversible hard-sphere Markov chains generalize to arbitrary repulsive pair interactions and carry over to higher dimensions through the concept of lifted Markov chains and the recently introduced factorized Metropolis acceptance rule.

Cost-Effectiveness of Different Cervical Screening Strategies in Islamic Republic of Iran: A Middle-Income Country with a Low Incidence Rate of Cervical Cancer.

PubMed

Nahvijou, Azin; Daroudi, Rajabali; Tahmasebi, Mamak; Amouzegar Hashemi, Farnaz; Rezaei Hemami, Mohsen; Akbari Sari, Ali; Barati Marenani, Ahmad; Zendehdel, Kazem

2016-01-01

Invasive cervical cancer (ICC) is the fourth most common cancer among women worldwide. Cervical screening programs have reduced the incidence and mortality rates of ICC. We studied the cost-effectiveness of different cervical screening strategies in the Islamic Republic of Iran, a Muslim country with a low incidence rate of ICC. We constructed an 11-state Markov model, in which the parameters included regression and progression probabilities, test characteristics, costs, and utilities; these were extracted from primary data and the literature. Our strategies included Pap smear screening and human papillomavirus (HPV) DNA testing plus Pap smear triaging with different starting ages and screening intervals. Model outcomes included lifetime costs, life years gained, quality-adjusted life years (QALY), and incremental cost-effectiveness ratios (ICERs). One-way sensitivity analysis was performed to examine the stability of the results. We found that the prevented mortalities for the 11 strategies compared with no screening varied from 26% to 64%. The most cost-effective strategy was HPV screening, starting at age 35 years and repeated every 10 years. The ICER of this strategy was $8,875 per QALY compared with no screening. We found that screening at 5-year intervals was also cost-effective based on GDP per capita in Iran. We recommend organized cervical screening with HPV DNA testing for women in Iran, beginning at age 35 and repeated every 10 or 5 years. The results of this study could be generalized to other countries with low incidence rates of cervical cancer.

Cost-Effectiveness of Different Cervical Screening Strategies in Islamic Republic of Iran: A Middle-Income Country with a Low Incidence Rate of Cervical Cancer

PubMed Central

Nahvijou, Azin; Daroudi, Rajabali; Tahmasebi, Mamak; Amouzegar Hashemi, Farnaz; Rezaei Hemami, Mohsen; Akbari Sari, Ali; Barati Marenani, Ahmad; Zendehdel, Kazem

2016-01-01

Objective Invasive cervical cancer (ICC) is the fourth most common cancer among women worldwide. Cervical screening programs have reduced the incidence and mortality rates of ICC. We studied the cost-effectiveness of different cervical screening strategies in the Islamic Republic of Iran, a Muslim country with a low incidence rate of ICC. Methods We constructed an 11-state Markov model, in which the parameters included regression and progression probabilities, test characteristics, costs, and utilities; these were extracted from primary data and the literature. Our strategies included Pap smear screening and human papillomavirus (HPV) DNA testing plus Pap smear triaging with different starting ages and screening intervals. Model outcomes included lifetime costs, life years gained, quality-adjusted life years (QALY), and incremental cost-effectiveness ratios (ICERs). One-way sensitivity analysis was performed to examine the stability of the results. Results We found that the prevented mortalities for the 11 strategies compared with no screening varied from 26% to 64%. The most cost-effective strategy was HPV screening, starting at age 35 years and repeated every 10 years. The ICER of this strategy was $8,875 per QALY compared with no screening. We found that screening at 5-year intervals was also cost-effective based on GDP per capita in Iran. Conclusion We recommend organized cervical screening with HPV DNA testing for women in Iran, beginning at age 35 and repeated every 10 or 5 years. The results of this study could be generalized to other countries with low incidence rates of cervical cancer. PMID:27276093

The cost-effectiveness of screening for colorectal cancer.

PubMed

Telford, Jennifer J; Levy, Adrian R; Sambrook, Jennifer C; Zou, Denise; Enns, Robert A

2010-09-07

Published decision analyses show that screening for colorectal cancer is cost-effective. However, because of the number of tests available, the optimal screening strategy in Canada is unknown. We estimated the incremental cost-effectiveness of 10 strategies for colorectal cancer screening, as well as no screening, incorporating quality of life, noncompliance and data on the costs and benefits of chemotherapy. We used a probabilistic Markov model to estimate the costs and quality-adjusted life expectancy of 50-year-old average-risk Canadians without screening and with screening by each test. We populated the model with data from the published literature. We calculated costs from the perspective of a third-party payer, with inflation to 2007 Canadian dollars. Of the 10 strategies considered, we focused on three tests currently being used for population screening in some Canadian provinces: low-sensitivity guaiac fecal occult blood test, performed annually; fecal immunochemical test, performed annually; and colonoscopy, performed every 10 years. These strategies reduced the incidence of colorectal cancer by 44%, 65% and 81%, and mortality by 55%, 74% and 83%, respectively, compared with no screening. These strategies generated incremental cost-effectiveness ratios of $9159, $611 and $6133 per quality-adjusted life year, respectively. The findings were robust to probabilistic sensitivity analysis. Colonoscopy every 10 years yielded the greatest net health benefit. Screening for colorectal cancer is cost-effective over conventional levels of willingness to pay. Annual high-sensitivity fecal occult blood testing, such as a fecal immunochemical test, or colonoscopy every 10 years offer the best value for the money in Canada.

A mathematical approach for evaluating Markov models in continuous time without discrete-event simulation.

PubMed

van Rosmalen, Joost; Toy, Mehlika; O'Mahony, James F

2013-08-01

Markov models are a simple and powerful tool for analyzing the health and economic effects of health care interventions. These models are usually evaluated in discrete time using cohort analysis. The use of discrete time assumes that changes in health states occur only at the end of a cycle period. Discrete-time Markov models only approximate the process of disease progression, as clinical events typically occur in continuous time. The approximation can yield biased cost-effectiveness estimates for Markov models with long cycle periods and if no half-cycle correction is made. The purpose of this article is to present an overview of methods for evaluating Markov models in continuous time. These methods use mathematical results from stochastic process theory and control theory. The methods are illustrated using an applied example on the cost-effectiveness of antiviral therapy for chronic hepatitis B. The main result is a mathematical solution for the expected time spent in each state in a continuous-time Markov model. It is shown how this solution can account for age-dependent transition rates and discounting of costs and health effects, and how the concept of tunnel states can be used to account for transition rates that depend on the time spent in a state. The applied example shows that the continuous-time model yields more accurate results than the discrete-time model but does not require much computation time and is easily implemented. In conclusion, continuous-time Markov models are a feasible alternative to cohort analysis and can offer several theoretical and practical advantages.

[Application of Markov model in post-marketing pharmacoeconomic evaluation of traditional Chinese medicine].

PubMed

Wang, Xin; Su, Xia; Sun, Wentao; Xie, Yanming; Wang, Yongyan

2011-10-01

In post-marketing study of traditional Chinese medicine (TCM), pharmacoeconomic evaluation has an important applied significance. However, the economic literatures of TCM have been unable to fully and accurately reflect the unique overall outcomes of treatment with TCM. For the special nature of TCM itself, we recommend that Markov model could be introduced into post-marketing pharmacoeconomic evaluation of TCM, and also explore the feasibility of model application. Markov model can extrapolate the study time horizon, suit with effectiveness indicators of TCM, and provide measurable comprehensive outcome. In addition, Markov model can promote the development of TCM quality of life scale and the methodology of post-marketing pharmacoeconomic evaluation.

A Lagrangian Transport Eulerian Reaction Spatial (LATERS) Markov Model for Prediction of Effective Bimolecular Reactive Transport

NASA Astrophysics Data System (ADS)

Sund, Nicole; Porta, Giovanni; Bolster, Diogo; Parashar, Rishi

2017-11-01

Prediction of effective transport for mixing-driven reactive systems at larger scales, requires accurate representation of mixing at small scales, which poses a significant upscaling challenge. Depending on the problem at hand, there can be benefits to using a Lagrangian framework, while in others an Eulerian might have advantages. Here we propose and test a novel hybrid model which attempts to leverage benefits of each. Specifically, our framework provides a Lagrangian closure required for a volume-averaging procedure of the advection diffusion reaction equation. This hybrid model is a LAgrangian Transport Eulerian Reaction Spatial Markov model (LATERS Markov model), which extends previous implementations of the Lagrangian Spatial Markov model and maps concentrations to an Eulerian grid to quantify closure terms required to calculate the volume-averaged reaction terms. The advantage of this approach is that the Spatial Markov model is known to provide accurate predictions of transport, particularly at preasymptotic early times, when assumptions required by traditional volume-averaging closures are least likely to hold; likewise, the Eulerian reaction method is efficient, because it does not require calculation of distances between particles. This manuscript introduces the LATERS Markov model and demonstrates by example its ability to accurately predict bimolecular reactive transport in a simple benchmark 2-D porous medium.

Cost-effectiveness analysis of population-based screening of hepatocellular carcinoma: Comparing ultrasonography with two-stage screening

PubMed Central

Kuo, Ming-Jeng; Chen, Hsiu-Hsi; Chen, Chi-Ling; Fann, Jean Ching-Yuan; Chen, Sam Li-Sheng; Chiu, Sherry Yueh-Hsia; Lin, Yu-Min; Liao, Chao-Sheng; Chang, Hung-Chuen; Lin, Yueh-Shih; Yen, Amy Ming-Fang

2016-01-01

AIM: To assess the cost-effectiveness of two population-based hepatocellular carcinoma (HCC) screening programs, two-stage biomarker-ultrasound method and mass screening using abdominal ultrasonography (AUS). METHODS: In this study, we applied a Markov decision model with a societal perspective and a lifetime horizon for the general population-based cohorts in an area with high HCC incidence, such as Taiwan. The accuracy of biomarkers and ultrasonography was estimated from published meta-analyses. The costs of surveillance, diagnosis, and treatment were based on a combination of published literature, Medicare payments, and medical expenditure at the National Taiwan University Hospital. The main outcome measure was cost per life-year gained with a 3% annual discount rate. RESULTS: The results show that the mass screening using AUS was associated with an incremental cost-effectiveness ratio of USD39825 per life-year gained, whereas two-stage screening was associated with an incremental cost-effectiveness ratio of USD49733 per life-year gained, as compared with no screening. Screening programs with an initial screening age of 50 years old and biennial screening interval were the most cost-effective. These findings were sensitive to the costs of screening tools and the specificity of biomarker screening. CONCLUSION: Mass screening using AUS is more cost effective than two-stage biomarker-ultrasound screening. The most optimal strategy is an initial screening age at 50 years old with a 2-year inter-screening interval. PMID:27022228

Impact of extending screening mammography to older women: Information to support informed choices.

PubMed

Jacklyn, Gemma; Howard, Kirsten; Irwig, Les; Houssami, Nehmat; Hersch, Jolyn; Barratt, Alexandra

2017-10-15

From 2013 through 2017, the Australian national breast cancer screening programme is gradually inviting women aged 70-74 years to attend screening, following a policy decision to extend invitations to older women. We estimate the benefits and harms of the new package of biennial screening from age 50-74 compared with the previous programme of screening from age 50-69. Using a Markov model, we applied estimates of the relative risk reduction for breast cancer mortality and the risk of overdiagnosis from the Independent UK Panel on Breast Cancer Screening review to Australian breast cancer incidence and mortality data. We estimated screening specific outcomes (recalls for further imaging, biopsies, false positives, and interval cancer rates) from data published by BreastScreen Australia. When compared with stopping at age 69, screening 1,000 women to age 74 is likely to avert one more breast cancer death, with an additional 78 women receiving a false positive result and another 28 women diagnosed with breast cancer, of whom eight will be overdiagnosed and overtreated. The extra 5 years of screening results in approximately 7 more overdiagnosed cancers to avert one more breast cancer death. Thus extending screening mammography in Australia to older women results in a less favourable harm to benefit ratio than stopping at age 69. Supporting informed decision making for this age group should be a public health priority. © 2017 UICC.

Markov and semi-Markov switching linear mixed models used to identify forest tree growth components.

PubMed

Chaubert-Pereira, Florence; Guédon, Yann; Lavergne, Christian; Trottier, Catherine

2010-09-01

Tree growth is assumed to be mainly the result of three components: (i) an endogenous component assumed to be structured as a succession of roughly stationary phases separated by marked change points that are asynchronous among individuals, (ii) a time-varying environmental component assumed to take the form of synchronous fluctuations among individuals, and (iii) an individual component corresponding mainly to the local environment of each tree. To identify and characterize these three components, we propose to use semi-Markov switching linear mixed models, i.e., models that combine linear mixed models in a semi-Markovian manner. The underlying semi-Markov chain represents the succession of growth phases and their lengths (endogenous component) whereas the linear mixed models attached to each state of the underlying semi-Markov chain represent-in the corresponding growth phase-both the influence of time-varying climatic covariates (environmental component) as fixed effects, and interindividual heterogeneity (individual component) as random effects. In this article, we address the estimation of Markov and semi-Markov switching linear mixed models in a general framework. We propose a Monte Carlo expectation-maximization like algorithm whose iterations decompose into three steps: (i) sampling of state sequences given random effects, (ii) prediction of random effects given state sequences, and (iii) maximization. The proposed statistical modeling approach is illustrated by the analysis of successive annual shoots along Corsican pine trunks influenced by climatic covariates. © 2009, The International Biometric Society.

Screen or not to screen for peripheral arterial disease: guidance from a decision model.

PubMed

Vaidya, Anil; Joore, Manuela A; Ten Cate-Hoek, Arina J; Ten Cate, Hugo; Severens, Johan L

2014-01-29

Asymptomatic Peripheral Arterial Disease (PAD) is associated with greater risk of acute cardiovascular events. This study aims to determine the cost-effectiveness of one time only PAD screening using Ankle Brachial Index (ABI) test and subsequent anti platelet preventive treatment (low dose aspirin or clopidogrel) in individuals at high risk for acute cardiovascular events compared to no screening and no treatment using decision analytic modelling. A probabilistic Markov model was developed to evaluate the life time cost-effectiveness of the strategy of selective PAD screening and consequent preventive treatment compared to no screening and no preventive treatment. The analysis was conducted from the Dutch societal perspective and to address decision uncertainty, probabilistic sensitivity analysis was performed. Results were based on average values of 1000 Monte Carlo simulations and using discount rates of 1.5% and 4% for effects and costs respectively. One way sensitivity analyses were performed to identify the two most influential model parameters affecting model outputs. Then, a two way sensitivity analysis was conducted for combinations of values tested for these two most influential parameters. For the PAD screening strategy, life years and quality adjusted life years gained were 21.79 and 15.66 respectively at a lifetime cost of 26,548 Euros. Compared to no screening and treatment (20.69 life years, 15.58 Quality Adjusted Life Years, 28,052 Euros), these results indicate that PAD screening and treatment is a dominant strategy. The cost effectiveness acceptability curves show 88% probability of PAD screening being cost effective at the Willingness To Pay (WTP) threshold of 40000 Euros. In a scenario analysis using clopidogrel as an alternative anti-platelet drug, PAD screening strategy remained dominant. This decision analysis suggests that targeted ABI screening and consequent secondary prevention of cardiovascular events using low dose aspirin or clopidogrel in the identified patients is a cost-effective strategy. Implementation of targeted PAD screening and subsequent treatment in primary care practices and in public health programs is likely to improve the societal health and to save health care costs by reducing catastrophic cardiovascular events.

Modeling of dialogue regimes of distance robot control

NASA Astrophysics Data System (ADS)

Larkin, E. V.; Privalov, A. N.

2017-02-01

Process of distance control of mobile robots is investigated. Petri-Markov net for modeling of dialogue regime is worked out. It is shown, that sequence of operations of next subjects: a human operator, a dialogue computer and an onboard computer may be simulated with use the theory of semi-Markov processes. From the semi-Markov process of the general form Markov process was obtained, which includes only states of transaction generation. It is shown, that a real transaction flow is the result of «concurrency» in states of Markov process. Iteration procedure for evaluation of transaction flow parameters, which takes into account effect of «concurrency», is proposed.

Cost effectiveness of mammography screening for Chinese women.

PubMed

Wong, Irene O L; Kuntz, Karen M; Cowling, Benjamin J; Lam, Cindy L K; Leung, Gabriel M

2007-08-15

Although the cost effectiveness of screening mammography in most western developed populations has been accepted, it may not apply to Chinese women, who have a much lower breast cancer incidence. The authors estimated the cost effectiveness of biennial mammography in Hong Kong Chinese women to inform evidence-based screening policies. For the current study, a state-transition Markov model was developed to simulate mammography screening, breast cancer diagnosis, and treatment in a hypothetical cohort of Chinese women. The benefit of mammography was modeled by assuming a stage shift, in which cancers in screened women were more likely to be diagnosed at an earlier disease stage. The authors compared costs, quality-adjusted life years (QALYs) saved, and life years saved (LYS) for 5 screening strategies. Biennial screening resulted in a gain in life expectancy ranging from 4.3 days to 9.4 days compared with no screening at an incremental cost of from US $1,166 to US $2,425 per woman. The least costly, nondominated screening option was screening from ages 40 years to 69 years, with an incremental cost-effectiveness ratio (ICER) of US $61,600 per QALY saved or US $64,400 per LYS compared with no screening. In probabilistic sensitivity analyses, the probability of the ICER being below a threshold of US $50,000 per QALY (LYS) was 15.3% (14.6%). The current results suggested that mammography for Hong Kong Chinese women currently may not be cost effective based on the arbitrary threshold of US $50,000 per QALY. However, clinicians must remain vigilant and periodically should revisit the question of population screening: Disease rates in China have been increasing because of westernization and socioeconomic development.

Cost-effectiveness of annual versus biennial screening mammography for women with high mammographic breast density.

PubMed

Pataky, Reka; Ismail, Zahra; Coldman, Andrew J; Elwood, Mark; Gelmon, Karen; Hedden, Lindsay; Hislop, Greg; Kan, Lisa; McCoy, Bonnie; Olivotto, Ivo A; Peacock, Stuart

2014-12-01

The sensitivity of screening mammography is much lower among women who have dense breast tissue, compared with women who have largely fatty breasts, and they are also at much higher risk of developing the disease. Increasing mammography screening frequency from biennially to annually has been suggested as a policy option to address the elevated risk in this population. The purpose of this study was to assess the cost-effectiveness of annual versus biennial screening mammography among women aged 50-79 with dense breast tissue. A Markov model was constructed based on screening, diagnostic, and treatment pathways for the population-based screening and cancer care programme in British Columbia, Canada. Model probabilities and screening costs were calculated from screening programme data. Costs for breast cancer treatment were calculated from treatment data, and utility values were obtained from the literature. Incremental cost-effectiveness was expressed as cost per quality adjusted life year (QALY), and probabilistic sensitivity analysis was conducted. Compared with biennial screening, annual screening generated an additional 0.0014 QALYs (95% CI: -0.0480-0.0359) at a cost of $819 ($ = Canadian dollars) per patient (95% CI: 506-1185), resulting in an incremental cost effectiveness ratio of $565,912/QALY. Annual screening had a 37.5% probability of being cost-effective at a willingness-to-pay threshold of $100,000/QALY. There is considerable uncertainty about the incremental cost-effectiveness of annual mammography. Further research on the comparative effectiveness of screening strategies for women with high mammographic breast density is warranted, particularly as digital mammography and density measurement become more widespread, before cost-effectiveness can be reevaluated. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Screening for Pancreatic Adenocarcinoma in BRCA2 Mutation Carriers: Results of a Disease Simulation Model.

PubMed

Pandharipande, Pari V; Jeon, Alvin; Heberle, Curtis R; Dowling, Emily C; Kong, Chung Yin; Chung, Daniel C; Brugge, William R; Hur, Chin

2015-12-01

BRCA2 mutation carriers are at increased risk for multiple cancers including pancreatic adenocarcinoma (PAC). Our goal was to compare the effectiveness of different PAC screening strategies in BRCA2 mutation carriers, from the standpoint of life expectancy. A previously published Markov model of PAC was updated and extended to incorporate key aspects of BRCA2 mutation carrier status, including competing risks of breast- and ovarian-cancer specific mortality. BRCA2 mutation carriers were modeled and analyzed as the primary cohort for the analysis. Additional higher risk BRCA2 cohorts that were stratified according to the number of first-degree relatives (FDRs) with PAC were also analyzed. For each cohort, one-time screening and annual screening were evaluated, with screening starting at age 50 in both strategies. The primary outcome was net gain in life expectancy (LE) compared to no screening. Sensitivity analysis was performed on key model parameters, including surgical mortality and MRI test performance. One-time screening at age 50 resulted in a LE gain of 3.9 days for the primary BRCA2 cohort, and a gain of 5.8 days for those with BRCA2 and one FDR. Annual screening resulted in LE loss of 12.9 days for the primary cohort and 1.3 days for BRCA2 carriers with 1 FDR, but resulted in 20.6 days gained for carriers with 2 FDRs and 260 days gained for those with 3 FDRs. For patients with ≥ 3 FDRs, annual screening starting at an earlier age (i.e. 35-40) was optimal. Among BRCA2 mutation carriers, aggressive screening regimens may be ineffective unless additional indicators of elevated risk (e.g., 2 or more FDRs) are present. More clinical studies are needed to confirm these findings. American Cancer Society - New England Division - Ellison Foundation Research Scholar Grant (RSG-15-129-01-CPHPS).

Multiensemble Markov models of molecular thermodynamics and kinetics.

PubMed

Wu, Hao; Paul, Fabian; Wehmeyer, Christoph; Noé, Frank

2016-06-07

We introduce the general transition-based reweighting analysis method (TRAM), a statistically optimal approach to integrate both unbiased and biased molecular dynamics simulations, such as umbrella sampling or replica exchange. TRAM estimates a multiensemble Markov model (MEMM) with full thermodynamic and kinetic information at all ensembles. The approach combines the benefits of Markov state models-clustering of high-dimensional spaces and modeling of complex many-state systems-with those of the multistate Bennett acceptance ratio of exploiting biased or high-temperature ensembles to accelerate rare-event sampling. TRAM does not depend on any rate model in addition to the widely used Markov state model approximation, but uses only fundamental relations such as detailed balance and binless reweighting of configurations between ensembles. Previous methods, including the multistate Bennett acceptance ratio, discrete TRAM, and Markov state models are special cases and can be derived from the TRAM equations. TRAM is demonstrated by efficiently computing MEMMs in cases where other estimators break down, including the full thermodynamics and rare-event kinetics from high-dimensional simulation data of an all-atom protein-ligand binding model.

A cost-effectiveness analysis of screening for silent atrial fibrillation after ischaemic stroke.

PubMed

Levin, Lars-Åke; Husberg, Magnus; Sobocinski, Piotr Doliwa; Kull, Viveka Frykman; Friberg, Leif; Rosenqvist, Mårten; Davidson, Thomas

2015-02-01

The purpose of this study was to estimate the cost-effectiveness of two screening methods for detection of silent AF, intermittent electrocardiogram (ECG) recordings using a handheld recording device, at regular time intervals for 30 days, and short-term 24 h continuous Holter ECG, in comparison with a no-screening alternative in 75-year-old patients with a recent ischaemic stroke. The long-term (20-year) costs and effects of all alternatives were estimated with a decision analytic model combining the result of a clinical study and epidemiological data from Sweden. The structure of a cost-effectiveness analysis was used in this study. The short-term decision tree model analysed the screening procedure until the onset of anticoagulant treatment. The second part of the decision model followed a Markov design, simulating the patients' health states for 20 years. Continuous 24 h ECG recording was inferior to intermittent ECG in terms of cost-effectiveness, due to both lower sensitivity and higher costs. The base-case analysis compared intermittent ECG screening with no screening of patients with recent stroke. The implementation of the screening programme on 1000 patients resulted over a 20-year period in 11 avoided strokes and the gain of 29 life-years, or 23 quality-adjusted life years, and cost savings of €55 400. Screening of silent AF by intermittent ECG recordings in patients with a recent ischaemic stroke is a cost-effective use of health care resources saving costs and lives and improving the quality of life. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2014. For permissions please email: journals.permissions@oup.com.

Master equation for She-Leveque scaling and its classification in terms of other Markov models of developed turbulence

NASA Astrophysics Data System (ADS)

Nickelsen, Daniel

2017-07-01

The statistics of velocity increments in homogeneous and isotropic turbulence exhibit universal features in the limit of infinite Reynolds numbers. After Kolmogorov’s scaling law from 1941, many turbulence models aim for capturing these universal features, some are known to have an equivalent formulation in terms of Markov processes. We derive the Markov process equivalent to the particularly successful scaling law postulated by She and Leveque. The Markov process is a jump process for velocity increments u(r) in scale r in which the jumps occur randomly but with deterministic width in u. From its master equation we establish a prescription to simulate the She-Leveque process and compare it with Kolmogorov scaling. To put the She-Leveque process into the context of other established turbulence models on the Markov level, we derive a diffusion process for u(r) using two properties of the Navier-Stokes equation. This diffusion process already includes Kolmogorov scaling, extended self-similarity and a class of random cascade models. The fluctuation theorem of this Markov process implies a ‘second law’ that puts a loose bound on the multipliers of the random cascade models. This bound explicitly allows for instances of inverse cascades, which are necessary to satisfy the fluctuation theorem. By adding a jump process to the diffusion process, we go beyond Kolmogorov scaling and formulate the most general scaling law for the class of Markov processes having both diffusion and jump parts. This Markov scaling law includes She-Leveque scaling and a scaling law derived by Yakhot.

Cost-effectiveness of computed tomography colonography in colorectal cancer screening: a systematic review.

PubMed

Hanly, Paul; Skally, Mairead; Fenlon, Helen; Sharp, Linda

2012-10-01

The European Code Against Cancer recommends individuals aged ≥ 50 should participate in colorectal cancer screening. CT-colonography (CTC) is one of several screening tests available. We systematically reviewed evidence on, and identified key factors influencing, cost-effectiveness of CTC screening. PubMed, Medline, and the Cochrane library were searched for cost-effectiveness or cost-utility analyses of CTC-based screening, published in English, January 1999 to July 2010. Data was abstracted on setting, model type and horizon, screening scenario(s), comparator(s), participants, uptake, CTC performance and cost, effectiveness, ICERs, and whether extra-colonic findings and medical complications were considered. Sixteen studies were identified from the United States (n = 11), Canada (n = 2), and France, Italy, and the United Kingdom (1 each). Markov state-transition (n = 14) or microsimulation (n = 2) models were used. Eleven considered direct medical costs only; five included indirect costs. Fourteen compared CTC with no screening; fourteen compared CTC with colonoscopy-based screening; fewer compared CTC with sigmoidoscopy (8) or fecal tests (4). Outcomes assessed were life-years gained/saved (13), QALYs (2), or both (1). Three considered extra-colonic findings; seven considered complications. CTC appeared cost-effective versus no screening and, in general, flexible sigmoidoscopy and fecal occult blood testing. Results were mixed comparing CTC to colonoscopy. Parameters most influencing cost-effectiveness included: CTC costs, screening uptake, threshold for polyp referral, and extra-colonic findings. Evidence on cost-effectiveness of CTC screening is heterogeneous, due largely to between-study differences in comparators and parameter values. Future studies should: compare CTC with currently favored tests, especially fecal immunochemical tests; consider extra-colonic findings; and conduct comprehensive sensitivity analyses.

Optimization of PSA screening policies: a comparison of the patient and societal perspectives.

PubMed

Zhang, Jingyu; Denton, Brian T; Balasubramanian, Hari; Shah, Nilay D; Inman, Brant A

2012-01-01

To estimate the benefit of PSA-based screening for prostate cancer from the patient and societal perspectives. A partially observable Markov decision process model was used to optimize PSA screening decisions. Age-specific prostate cancer incidence rates and the mortality rates from prostate cancer and competing causes were considered. The model trades off the potential benefit of early detection with the cost of screening and loss of patient quality of life due to screening and treatment. PSA testing and biopsy decisions are made based on the patient's probability of having prostate cancer. Probabilities are inferred based on the patient's complete PSA history using Bayesian updating. The results of all PSA tests and biopsies done in Olmsted County, Minnesota, from 1993 to 2005 (11,872 men and 50,589 PSA test results). Patients' perspective: to maximize expected quality-adjusted life years (QALYs); societal perspective: to maximize the expected monetary value based on societal willingness to pay for QALYs and the cost of PSA testing, prostate biopsies, and treatment. From the patient perspective, the optimal policy recommends stopping PSA testing and biopsy at age 76. From the societal perspective, the stopping age is 71. The expected incremental benefit of optimal screening over the traditional guideline of annual PSA screening with threshold 4.0 ng/mL for biopsy is estimated to be 0.165 QALYs per person from the patient perspective and 0.161 QALYs per person from the societal perspective. PSA screening based on traditional guidelines is found to be worse than no screening at all. PSA testing done with traditional guidelines underperforms and therefore underestimates the potential benefit of screening. Optimal screening guidelines differ significantly depending on the perspective of the decision maker.

Economic model of a birth cohort screening program for hepatitis C virus.

PubMed

McGarry, Lisa J; Pawar, Vivek S; Panchmatia, Hemangi R; Rubin, Jaime L; Davis, Gary L; Younossi, Zobair M; Capretta, James C; O'Grady, Michael J; Weinstein, Milton C

2012-05-01

Recent research has identified high hepatitis C virus (HCV) prevalence among older U.S. residents who contracted HCV decades ago and may no longer be recognized as high risk. We assessed the cost-effectiveness of screening 100% of U.S. residents born 1946-1970 over 5 years (birth-cohort screening), compared with current risk-based screening, by projecting costs and outcomes of screening over the remaining lifetime of this birth cohort. A Markov model of the natural history of HCV was developed using data synthesized from surveillance data, published literature, expert opinion, and other secondary sources. We assumed eligible patients were treated with pegylated interferon plus ribavirin, with genotype 1 patients receiving a direct-acting antiviral in combination. The target population is U.S. residents born 1946-1970 with no previous HCV diagnosis. Among the estimated 102 million (1.6 million chronically HCV infected) eligible for screening, birth-cohort screening leads to 84,000 fewer cases of decompensated cirrhosis, 46,000 fewer cases of hepatocellular carcinoma, 10,000 fewer liver transplants, and 78,000 fewer HCV-related deaths. Birth-cohort screening leads to higher overall costs than risk-based screening ($80.4 billion versus $53.7 billion), but yields lower costs related to advanced liver disease ($31.2 billion versus $39.8 billion); birth-cohort screening produces an incremental cost-effectiveness ratio (ICER) of $37,700 per quality-adjusted life year gained versus risk-based screening. Sensitivity analyses showed that reducing the time horizon during which health and economic consequences are evaluated increases the ICER; similarly, decreasing the treatment rates and efficacy increases the ICER. Model results were relatively insensitive to other inputs. Birth-cohort screening for HCV is likely to provide important health benefits by reducing lifetime cases of advanced liver disease and HCV-related deaths and is cost-effective at conventional willingness-to-pay thresholds. Copyright © 2011 American Association for the Study of Liver Diseases.

Multiensemble Markov models of molecular thermodynamics and kinetics

PubMed Central

Wu, Hao; Paul, Fabian; Noé, Frank

2016-01-01

We introduce the general transition-based reweighting analysis method (TRAM), a statistically optimal approach to integrate both unbiased and biased molecular dynamics simulations, such as umbrella sampling or replica exchange. TRAM estimates a multiensemble Markov model (MEMM) with full thermodynamic and kinetic information at all ensembles. The approach combines the benefits of Markov state models—clustering of high-dimensional spaces and modeling of complex many-state systems—with those of the multistate Bennett acceptance ratio of exploiting biased or high-temperature ensembles to accelerate rare-event sampling. TRAM does not depend on any rate model in addition to the widely used Markov state model approximation, but uses only fundamental relations such as detailed balance and binless reweighting of configurations between ensembles. Previous methods, including the multistate Bennett acceptance ratio, discrete TRAM, and Markov state models are special cases and can be derived from the TRAM equations. TRAM is demonstrated by efficiently computing MEMMs in cases where other estimators break down, including the full thermodynamics and rare-event kinetics from high-dimensional simulation data of an all-atom protein–ligand binding model. PMID:27226302

The Clinical and Economic Benefits of Co-Testing Versus Primary HPV Testing for Cervical Cancer Screening: A Modeling Analysis.

PubMed

Felix, Juan C; Lacey, Michael J; Miller, Jeffrey D; Lenhart, Gregory M; Spitzer, Mark; Kulkarni, Rucha

2016-06-01

Consensus United States cervical cancer screening guidelines recommend use of combination Pap plus human papillomavirus (HPV) testing for women aged 30 to 65 years. An HPV test was approved by the Food and Drug Administration in 2014 for primary cervical cancer screening in women age 25 years and older. Here, we present the results of clinical-economic comparisons of Pap plus HPV mRNA testing including genotyping for HPV 16/18 (co-testing) versus DNA-based primary HPV testing with HPV 16/18 genotyping and reflex cytology (HPV primary) for cervical cancer screening. A health state transition (Markov) model with 1-year cycling was developed using epidemiologic, clinical, and economic data from healthcare databases and published literature. A hypothetical cohort of one million women receiving triennial cervical cancer screening was simulated from ages 30 to 70 years. Screening strategies compared HPV primary to co-testing. Outcomes included total and incremental differences in costs, invasive cervical cancer (ICC) cases, ICC deaths, number of colposcopies, and quality-adjusted life years for cost-effectiveness calculations. Comprehensive sensitivity analyses were performed. In a simulation cohort of one million 30-year-old women modeled up to age 70 years, the model predicted that screening with HPV primary testing instead of co-testing could lead to as many as 2,141 more ICC cases and 2,041 more ICC deaths. In the simulation, co-testing demonstrated a greater number of lifetime quality-adjusted life years (22,334) and yielded $39.0 million in savings compared with HPV primary, thereby conferring greater effectiveness at lower cost. Model results demonstrate that co-testing has the potential to provide improved clinical and economic outcomes when compared with HPV primary. While actual cost and outcome data are evaluated, these findings are relevant to U.S. healthcare payers and women's health policy advocates seeking cost-effective cervical cancer screening technologies.

Markov stochasticity coordinates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eliazar, Iddo, E-mail: iddo.eliazar@intel.com

Markov dynamics constitute one of the most fundamental models of random motion between the states of a system of interest. Markov dynamics have diverse applications in many fields of science and engineering, and are particularly applicable in the context of random motion in networks. In this paper we present a two-dimensional gauging method of the randomness of Markov dynamics. The method–termed Markov Stochasticity Coordinates–is established, discussed, and exemplified. Also, the method is tweaked to quantify the stochasticity of the first-passage-times of Markov dynamics, and the socioeconomic equality and mobility in human societies.

Markov modeling for the neurosurgeon: a review of the literature and an introduction to cost-effectiveness research.

PubMed

Wali, Arvin R; Brandel, Michael G; Santiago-Dieppa, David R; Rennert, Robert C; Steinberg, Jeffrey A; Hirshman, Brian R; Murphy, James D; Khalessi, Alexander A

2018-05-01

OBJECTIVE Markov modeling is a clinical research technique that allows competing medical strategies to be mathematically assessed in order to identify the optimal allocation of health care resources. The authors present a review of the recently published neurosurgical literature that employs Markov modeling and provide a conceptual framework with which to evaluate, critique, and apply the findings generated from health economics research. METHODS The PubMed online database was searched to identify neurosurgical literature published from January 2010 to December 2017 that had utilized Markov modeling for neurosurgical cost-effectiveness studies. Included articles were then assessed with regard to year of publication, subspecialty of neurosurgery, decision analytical techniques utilized, and source information for model inputs. RESULTS A total of 55 articles utilizing Markov models were identified across a broad range of neurosurgical subspecialties. Sixty-five percent of the papers were published within the past 3 years alone. The majority of models derived health transition probabilities, health utilities, and cost information from previously published studies or publicly available information. Only 62% of the studies incorporated indirect costs. Ninety-three percent of the studies performed a 1-way or 2-way sensitivity analysis, and 67% performed a probabilistic sensitivity analysis. A review of the conceptual framework of Markov modeling and an explanation of the different terminology and methodology are provided. CONCLUSIONS As neurosurgeons continue to innovate and identify novel treatment strategies for patients, Markov modeling will allow for better characterization of the impact of these interventions on a patient and societal level. The aim of this work is to equip the neurosurgical readership with the tools to better understand, critique, and apply findings produced from cost-effectiveness research.

Three real-time architectures - A study using reward models

NASA Technical Reports Server (NTRS)

Sjogren, J. A.; Smith, R. M.

1990-01-01

Numerous applications in the area of computer system analysis can be effectively studied with Markov reward models. These models describe the evolutionary behavior of the computer system by a continuous-time Markov chain, and a reward rate is associated with each state. In reliability/availability models, upstates have reward rate 1, and down states have reward rate zero associated with them. In a combined model of performance and reliability, the reward rate of a state may be the computational capacity, or a related performance measure. Steady-state expected reward rate and expected instantaneous reward rate are clearly useful measures which can be extracted from the Markov reward model. The diversity of areas where Markov reward models may be used is illustrated with a comparative study of three examples of interest to the fault tolerant computing community.

Markov chains and semi-Markov models in time-to-event analysis.

PubMed

Abner, Erin L; Charnigo, Richard J; Kryscio, Richard J

2013-10-25

A variety of statistical methods are available to investigators for analysis of time-to-event data, often referred to as survival analysis. Kaplan-Meier estimation and Cox proportional hazards regression are commonly employed tools but are not appropriate for all studies, particularly in the presence of competing risks and when multiple or recurrent outcomes are of interest. Markov chain models can accommodate censored data, competing risks (informative censoring), multiple outcomes, recurrent outcomes, frailty, and non-constant survival probabilities. Markov chain models, though often overlooked by investigators in time-to-event analysis, have long been used in clinical studies and have widespread application in other fields.

Markov chains and semi-Markov models in time-to-event analysis

PubMed Central

Abner, Erin L.; Charnigo, Richard J.; Kryscio, Richard J.

2014-01-01

A variety of statistical methods are available to investigators for analysis of time-to-event data, often referred to as survival analysis. Kaplan-Meier estimation and Cox proportional hazards regression are commonly employed tools but are not appropriate for all studies, particularly in the presence of competing risks and when multiple or recurrent outcomes are of interest. Markov chain models can accommodate censored data, competing risks (informative censoring), multiple outcomes, recurrent outcomes, frailty, and non-constant survival probabilities. Markov chain models, though often overlooked by investigators in time-to-event analysis, have long been used in clinical studies and have widespread application in other fields. PMID:24818062

[Development of Markov models for economics evaluation of strategies on hepatitis B vaccination and population-based antiviral treatment in China].

PubMed

Yang, P C; Zhang, S X; Sun, P P; Cai, Y L; Lin, Y; Zou, Y H

2017-07-10

Objective: To construct the Markov models to reflect the reality of prevention and treatment interventions against hepatitis B virus (HBV) infection, simulate the natural history of HBV infection in different age groups and provide evidence for the economics evaluations of hepatitis B vaccination and population-based antiviral treatment in China. Methods: According to the theory and techniques of Markov chain, the Markov models of Chinese HBV epidemic were developed based on the national data and related literature both at home and abroad, including the settings of Markov model states, allowable transitions and initial and transition probabilities. The model construction, operation and verification were conducted by using software TreeAge Pro 2015. Results: Several types of Markov models were constructed to describe the disease progression of HBV infection in neonatal period, perinatal period or adulthood, the progression of chronic hepatitis B after antiviral therapy, hepatitis B prevention and control in adults, chronic hepatitis B antiviral treatment and the natural progression of chronic hepatitis B in general population. The model for the newborn was fundamental which included ten states, i.e . susceptiblity to HBV, HBsAg clearance, immune tolerance, immune clearance, low replication, HBeAg negative CHB, compensated cirrhosis, decompensated cirrhosis, hepatocellular carcinoma (HCC) and death. The susceptible state to HBV was excluded in the perinatal period model, and the immune tolerance state was excluded in the adulthood model. The model for general population only included two states, survive and death. Among the 5 types of models, there were 9 initial states assigned with initial probabilities, and 27 states for transition probabilities. The results of model verifications showed that the probability curves were basically consistent with the situation of HBV epidemic in China. Conclusion: The Markov models developed can be used in economics evaluation of hepatitis B vaccination and treatment for the elimination of HBV infection in China though the structures and parameters in the model have uncertainty with dynamic natures.

Targeted screening and treatment for latent tuberculosis infection using QuantiFERON-TB Gold is cost-effective in Mexico.

PubMed

Burgos, J L; Kahn, J G; Strathdee, S A; Valencia-Mendoza, A; Bautista-Arredondo, S; Laniado-Laborin, R; Castañeda, R; Deiss, R; Garfein, R S

2009-08-01

To assess the cost-effectiveness of screening for latent tuberculosis infection (LTBI) using a commercially available detection test and treating individuals at high risk for human immunodeficiency virus (HIV) infection in a middle-income country. We developed a Markov model to evaluate the cost per LTBI case detected, TB case averted and quality-adjusted life year (QALY) gained for a cohort of 1000 individuals at high risk for HIV infection over 20 years. Baseline model inputs for LTBI prevalence were obtained from published literature and cross-sectional data from tuberculosis (TB) screening using QuantiFERON-TB Gold In-Tube (QFT-GIT) testing among sex workers and illicit drug users at high risk for HIV recruited through street outreach in Tijuana, Mexico. Costs are reported in 2007 US dollars. Future costs and QALYs were discounted at 3% per year. Sensitivity analyses were performed to evaluate model robustness. Over 20 years, we estimate the program would prevent 78 cases of active TB and 55 TB-related deaths. The incremental cost per case of LTBI detected was US$730, cost per active TB averted was US$529 and cost per QALY gained was US$108. In settings of endemic TB and escalating HIV incidence, targeting LTBI screening and treatment among high-risk groups may be highly cost-effective.

Targeted screening and treatment for latent tuberculosis infection using QuantiFERON®-TB Gold is cost-effective in Mexico

PubMed Central

Burgos, J. L.; Kahn, J. G.; Strathdee, S. A.; Valencia-Mendoza, A.; Bautista-Arredondo, S.; Laniado-Laborin, R.; Castañeda, R.; Deiss, R.; Garfein, R. S.

2009-01-01

SUMMARY OBJECTIVE To assess the cost-effectiveness of screening for latent tuberculosis infection (LTBI) using a commercially available detection test and treating individuals at high risk for human immunodeficiency virus (HIV) infection in a middle-income country. DESIGN We developed a Markov model to evaluate the cost per LTBI case detected, TB case averted and quality-adjusted life year (QALY) gained for a cohort of 1000 individuals at high risk for HIV infection over 20 years. Baseline model inputs for LTBI prevalence were obtained from published literature and cross-sectional data from tuberculosis (TB) screening using QuantiFERON®-TB Gold In-Tube (QFT-GIT) testing among sex workers and illicit drug users at high risk for HIV recruited through street outreach in Tijuana, Mexico. Costs are reported in 2007 US dollars. Future costs and QALYs were discounted at 3% per year. Sensitivity analyses were performed to evaluate model robustness. RESULTS Over 20 years, we estimate the program would prevent 78 cases of active TB and 55 TB-related deaths. The incremental cost per case of LTBI detected was US$730, cost per active TB averted was US$529 and cost per QALY gained was US$108. CONCLUSIONS In settings of endemic TB and escalating HIV incidence, targeting LTBI screening and treatment among high-risk groups may be highly cost-effective. PMID:19723375

Nonparametric model validations for hidden Markov models with applications in financial econometrics.

PubMed

Zhao, Zhibiao

2011-06-01

We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise.

Enhancement of Markov chain model by integrating exponential smoothing: A case study on Muslims marriage and divorce

NASA Astrophysics Data System (ADS)

Jamaluddin, Fadhilah; Rahim, Rahela Abdul

2015-12-01

Markov Chain has been introduced since the 1913 for the purpose of studying the flow of data for a consecutive number of years of the data and also forecasting. The important feature in Markov Chain is obtaining the accurate Transition Probability Matrix (TPM). However to obtain the suitable TPM is hard especially in involving long-term modeling due to unavailability of data. This paper aims to enhance the classical Markov Chain by introducing Exponential Smoothing technique in developing the appropriate TPM.

Timing of testing and treatment for asymptomatic diseases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kırkızlar, Eser; Faissol, Daniel M.; Griffin, Paul M.

2010-07-01

Many papers in the medical literature analyze the cost-effectiveness of screening for diseases by comparing a limited number of a priori testing policies under estimated problem parameters. However, this may be insufficient to determine the best timing of the tests or incorporate changes over time. In this paper, we develop and solve a Markov Decision Process (MDP) model for a simple class of asymptomatic diseases in order to provide the building blocks for analysis of a more general class of diseases. We provide a computationally efficient method for determining a cost-effective dynamic intervention strategy that takes into account (i) themore » results of the previous test for each individual and (ii) the change in the individual’s behavior based on awareness of the disease. We demonstrate the usefulness of the approach by applying the results to screening decisions for Hepatitis C (HCV) using medical data, and compare our findings to current HCV screening recommendations.« less

Fuzzy Markov random fields versus chains for multispectral image segmentation.

PubMed

Salzenstein, Fabien; Collet, Christophe

2006-11-01

This paper deals with a comparison of recent statistical models based on fuzzy Markov random fields and chains for multispectral image segmentation. The fuzzy scheme takes into account discrete and continuous classes which model the imprecision of the hidden data. In this framework, we assume the dependence between bands and we express the general model for the covariance matrix. A fuzzy Markov chain model is developed in an unsupervised way. This method is compared with the fuzzy Markovian field model previously proposed by one of the authors. The segmentation task is processed with Bayesian tools, such as the well-known MPM (Mode of Posterior Marginals) criterion. Our goal is to compare the robustness and rapidity for both methods (fuzzy Markov fields versus fuzzy Markov chains). Indeed, such fuzzy-based procedures seem to be a good answer, e.g., for astronomical observations when the patterns present diffuse structures. Moreover, these approaches allow us to process missing data in one or several spectral bands which correspond to specific situations in astronomy. To validate both models, we perform and compare the segmentation on synthetic images and raw multispectral astronomical data.

Developing a Markov Model for Forecasting End Strength of Selected Marine Corps Reserve (SMCR) Officers

DTIC Science & Technology

2013-03-01

moving average ( ARIMA ) model because the data is not a times series. The best a manpower planner can do at this point is to make an educated assumption...MARKOV MODEL FOR FORECASTING END STRENGTH OF SELECTED MARINE CORPS RESERVE (SMCR) OFFICERS by Anthony D. Licari March 2013 Thesis Advisor...March 2013 3. REPORT TYPE AND DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE DEVELOPING A MARKOV MODEL FOR FORECASTING END STRENGTH OF

Low-dose chest computed tomography for lung cancer screening among Hodgkin lymphoma survivors: a cost-effectiveness analysis.

PubMed

Wattson, Daniel A; Hunink, M G Myriam; DiPiro, Pamela J; Das, Prajnan; Hodgson, David C; Mauch, Peter M; Ng, Andrea K

2014-10-01

Hodgkin lymphoma (HL) survivors face an increased risk of treatment-related lung cancer. Screening with low-dose computed tomography (LDCT) may allow detection of early stage, resectable cancers. We developed a Markov decision-analytic and cost-effectiveness model to estimate the merits of annual LDCT screening among HL survivors. Population databases and HL-specific literature informed key model parameters, including lung cancer rates and stage distribution, cause-specific survival estimates, and utilities. Relative risks accounted for radiation therapy (RT) technique, smoking status (>10 pack-years or current smokers vs not), age at HL diagnosis, time from HL treatment, and excess radiation from LDCTs. LDCT assumptions, including expected stage-shift, false-positive rates, and likely additional workup were derived from the National Lung Screening Trial and preliminary results from an internal phase 2 protocol that performed annual LDCTs in 53 HL survivors. We assumed a 3% discount rate and a willingness-to-pay (WTP) threshold of $50,000 per quality-adjusted life year (QALY). Annual LDCT screening was cost effective for all smokers. A male smoker treated with mantle RT at age 25 achieved maximum QALYs by initiating screening 12 years post-HL, with a life expectancy benefit of 2.1 months and an incremental cost of $34,841/QALY. Among nonsmokers, annual screening produced a QALY benefit in some cases, but the incremental cost was not below the WTP threshold for any patient subsets. As age at HL diagnosis increased, earlier initiation of screening improved outcomes. Sensitivity analyses revealed that the model was most sensitive to the lung cancer incidence and mortality rates and expected stage-shift from screening. HL survivors are an important high-risk population that may benefit from screening, especially those treated in the past with large radiation fields including mantle or involved-field RT. Screening may be cost effective for all smokers but possibly not for nonsmokers despite a small life expectancy benefit. Copyright © 2014 Elsevier Inc. All rights reserved.

Markov chains for testing redundant software

NASA Technical Reports Server (NTRS)

White, Allan L.; Sjogren, Jon A.

1988-01-01

A preliminary design for a validation experiment has been developed that addresses several problems unique to assuring the extremely high quality of multiple-version programs in process-control software. The procedure uses Markov chains to model the error states of the multiple version programs. The programs are observed during simulated process-control testing, and estimates are obtained for the transition probabilities between the states of the Markov chain. The experimental Markov chain model is then expanded into a reliability model that takes into account the inertia of the system being controlled. The reliability of the multiple version software is computed from this reliability model at a given confidence level using confidence intervals obtained for the transition probabilities during the experiment. An example demonstrating the method is provided.

Estimating the Cost-Effectiveness of One-Time Screening and Treatment for Hepatitis C in Korea

PubMed Central

Kim, Do Young; Han, Kwang-Hyub; Jun, Byungyool; Kim, Tae Hyun; Park, Sohee; Ward, Thomas; Webster, Samantha; McEwan, Phil

2017-01-01

Background and Aims This study aims to investigate the cost-effectiveness of a one-time hepatitis C virus (HCV) screening and treatment program in South Korea where hepatitis B virus (HBV) prevails, in people aged 40–70, compared to current practice (no screening). Methods A published Markov model was used in conjunction with a screening and treatment decision tree to model patient cohorts, aged 40–49, 50–59 and 60–69 years, distributed across chronic hepatitis C (CHC) and compensated cirrhosis (CC) health states (82.5% and 17.5%, respectively). Based on a published seroepidemiology study, HCV prevalence was estimated at 0.60%, 0.80% and 1.53%, respectively. An estimated 71.7% of the population was screened. Post-diagnosis, 39.4% of patients were treated with a newly available all-oral direct-acting antiviral (DAA) regimen over 5 years. Published rates of sustained virologic response, disease management costs, transition rates and utilities were utilised. Results Screening resulted in the identification of 43,635 previously undiagnosed patients across all cohorts. One-time HCV screening and treatment was estimated to be cost-effective across all cohorts; predicted incremental cost-effectiveness ratios ranged from $5,714 to $8,889 per quality-adjusted life year gained. Incremental costs associated with screening, treatment and disease management ranged from $156.47 to $181.85 million USD; lifetime costs-offsets associated with the avoidance of end stage liver disease complications ranged from $51.47 to $57.48 million USD. Conclusions One-time HCV screening and treatment in South Korean people aged 40–70 is likely to be highly cost-effective compared to the current practice of no screening. PMID:28060834

Cost-effectiveness of an advance notification letter to increase colorectal cancer screening.

PubMed

Cronin, Paula; Goodall, Stephen; Lockett, Trevor; O'Keefe, Christine M; Norman, Richard; Church, Jody

2013-07-01

The aim of this study is to evaluate the cost-effectiveness of a patient-direct mailed advance notification letter on participants of a National Bowel Cancer Screening Program (NBCSP) in Australia, which was launched in August 2006 and offers free fecal occult blood testing to all Australians turning 50, 55, or 65 years of age in any given year. This study followed a hypothetical cohort of 50-year-old, 55-year-old, and 65-year-old patients undergoing fecal occult blood test (FOBT) screening through a decision analytic Markov model. The intervention compared two strategies: (i) advance letter, NBCSP, and FOBT compared with (ii) NBCSP and FOBT. The main outcome measures were life-years gained (LYG), quality-adjusted life-years (QALYs) gained and incremental cost-effectiveness ratio. An advance notification screening letter would yield an additional 54 per 100,000 colorectal cancer deaths avoided compared with no letter. The estimated cost-effectiveness was $3,976 per LYG and $6,976 per QALY gained. An advance notification letter in the NBCSP may have a significant impact on LYG and cancer deaths avoided. It is cost-effective and offers a feasible strategy that could be rolled out across other screening program at an acceptable cost.

Markov reward processes

NASA Technical Reports Server (NTRS)

Smith, R. M.

1991-01-01

Numerous applications in the area of computer system analysis can be effectively studied with Markov reward models. These models describe the behavior of the system with a continuous-time Markov chain, where a reward rate is associated with each state. In a reliability/availability model, upstates may have reward rate 1 and down states may have reward rate zero associated with them. In a queueing model, the number of jobs of certain type in a given state may be the reward rate attached to that state. In a combined model of performance and reliability, the reward rate of a state may be the computational capacity, or a related performance measure. Expected steady-state reward rate and expected instantaneous reward rate are clearly useful measures of the Markov reward model. More generally, the distribution of accumulated reward or time-averaged reward over a finite time interval may be determined from the solution of the Markov reward model. This information is of great practical significance in situations where the workload can be well characterized (deterministically, or by continuous functions e.g., distributions). The design process in the development of a computer system is an expensive and long term endeavor. For aerospace applications the reliability of the computer system is essential, as is the ability to complete critical workloads in a well defined real time interval. Consequently, effective modeling of such systems must take into account both performance and reliability. This fact motivates our use of Markov reward models to aid in the development and evaluation of fault tolerant computer systems.

Modelisation de l'historique d'operation de groupes turbine-alternateur

NASA Astrophysics Data System (ADS)

Szczota, Mickael

Because of their ageing fleet, the utility managers are increasingly in needs of tools that can help them to plan efficiently maintenance operations. Hydro-Quebec started a project that aim to foresee the degradation of their hydroelectric runner, and use that information to classify the generating unit. That classification will help to know which generating unit is more at risk to undergo a major failure. Cracks linked to the fatigue phenomenon are a predominant degradation mode and the loading sequences applied to the runner is a parameter impacting the crack growth. So, the aim of this memoir is to create a generator able to generate synthetic loading sequences that are statistically equivalent to the observed history. Those simulated sequences will be used as input in a life assessment model. At first, we describe how the generating units are operated by Hydro-Quebec and analyse the available data, the analysis shows that the data are non-stationnary. Then, we review modelisation and validation methods. In the following chapter a particular attention is given to a precise description of the validation and comparison procedure. Then, we present the comparison of three kind of model : Discrete Time Markov Chains, Discrete Time Semi-Markov Chains and the Moving Block Bootstrap. For the first two models, we describe how to take account for the non-stationnarity. Finally, we show that the Markov Chain is not adapted for our case, and that the Semi-Markov chains are better when they include the non-stationnarity. The final choice between Semi-Markov Chains and the Moving Block Bootstrap depends of the user. But, with a long term vision we recommend the use of Semi-Markov chains for their flexibility. Keywords: Stochastic models, Models validation, Reliability, Semi-Markov Chains, Markov Chains, Bootstrap

Cost-effectiveness of a National Telemedicine Diabetic Retinopathy Screening Program in Singapore.

PubMed

Nguyen, Hai V; Tan, Gavin Siew Wei; Tapp, Robyn Jennifer; Mital, Shweta; Ting, Daniel Shu Wei; Wong, Hon Tym; Tan, Colin S; Laude, Augustinus; Tai, E Shyong; Tan, Ngiap Chuan; Finkelstein, Eric A; Wong, Tien Yin; Lamoureux, Ecosse L

2016-12-01

To determine the incremental cost-effectiveness of a new telemedicine technician-based assessment relative to an existing model of family physician (FP)-based assessment of diabetic retinopathy (DR) in Singapore from the health system and societal perspectives. Model-based, cost-effectiveness analysis of the Singapore Integrated Diabetic Retinopathy Program (SiDRP). A hypothetical cohort of patients aged 55 years with type 2 diabetes previously not screened for DR. The SiDRP is a new telemedicine-based DR screening program using trained technicians to assess retinal photographs. We compared the cost-effectiveness of SiDRP with the existing model in which FPs assess photographs. We developed a hybrid decision tree/Markov model to simulate the costs, effectiveness, and incremental cost-effectiveness ratio (ICER) of SiDRP relative to FP-based DR screening over a lifetime horizon. We estimated the costs from the health system and societal perspectives. Effectiveness was measured in terms of quality-adjusted life-years (QALYs). Result robustness was calculated using deterministic and probabilistic sensitivity analyses. The ICER. From the societal perspective that takes into account all costs and effects, the telemedicine-based DR screening model had significantly lower costs (total cost savings of S$173 per person) while generating similar QALYs compared with the physician-based model (i.e., 13.1 QALYs). From the health system perspective that includes only direct medical costs, the cost savings are S$144 per person. By extrapolating these data to approximately 170 000 patients with diabetes currently being screened yearly for DR in Singapore's primary care polyclinics, the present value of future cost savings associated with the telemedicine-based model is estimated to be S$29.4 million over a lifetime horizon. While generating similar health outcomes, the telemedicine-based DR screening using technicians in the primary care setting saves costs for Singapore compared with the FP model. Our data provide a strong economic rationale to expand the telemedicine-based DR screening program in Singapore and elsewhere. Copyright © 2016 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

High-resolution microendoscopy for esophageal cancer screening in China: A cost-effectiveness analysis.

PubMed

Hur, Chin; Choi, Sung Eun; Kong, Chung Yin; Wang, Gui-Qi; Xu, Hong; Polydorides, Alexandros D; Xue, Li-Yan; Perzan, Katherine E; Tramontano, Angela C; Richards-Kortum, Rebecca R; Anandasabapathy, Sharmila

2015-05-14

To study the cost-effectiveness of high-resolution microendoscopy (HRME) in an esophageal squamous cell carcinoma (ESCC) screening program in China. A decision analytic Markov model of ESCC was developed. Separate model analyses were conducted for cohorts consisting of an average-risk population or a high-risk population in China. Hypothetical 50-year-old individuals were followed until age 80 or death. We compared three different strategies for both cohorts: (1) no screening; (2) standard endoscopic screening with Lugol's iodine staining; and (3) endoscopic screening with Lugol's iodine staining and an HRME. Model parameters were estimated from the literature as well as from GLOBOCAN, the Cancer Incidence and Mortality Worldwide cancer database. Health states in the model included non-neoplasia, mild dysplasia, moderate dysplasia, high-grade dysplasia, intramucosal carcinoma, operable cancer, inoperable cancer, and death. Separate ESCC incidence transition rates were generated for the average-risk and high-risk populations. Costs in Chinese currency were converted to international dollars (I$) and were adjusted to 2012 dollars using the Consumer Price Index. The main outcome measurements for this study were quality-adjusted life years (QALYs) and incremental cost-effectiveness ratio (ICER). For the average-risk population, the HRME screening strategy produced 0.043 more QALYs than the no screening strategy at an additional cost of I$646, resulting in an ICER of I$11808 per QALY gained. Standard endoscopic screening was weakly dominated. Among the high-risk population, when the HRME screening strategy was compared with the standard screening strategy, the ICER was I$8173 per QALY. For both the high-risk and average-risk screening populations, the HRME screening strategy appeared to be the most cost-effective strategy, producing ICERs below the willingness-to-pay threshold, I$23500 per QALY. One-way sensitivity analysis showed that, for the average-risk population, higher specificity of Lugol's iodine (> 40%) and lower specificity of HRME (< 70%) could make Lugol's iodine screening cost-effective. For the high-risk population, the results of the model were not substantially affected by varying the follow-up rate after Lugol's iodine screening, Lugol's iodine test characteristics (sensitivity and specificity), or HRME specificity. The incorporation of HRME into an ESCC screening program could be cost-effective in China. Larger studies of HRME performance are needed to confirm these findings.

Machine learning in sentiment reconstruction of the simulated stock market

NASA Astrophysics Data System (ADS)

Goykhman, Mikhail; Teimouri, Ali

2018-02-01

In this paper we continue the study of the simulated stock market framework defined by the driving sentiment processes. We focus on the market environment driven by the buy/sell trading sentiment process of the Markov chain type. We apply the methodology of the Hidden Markov Models and the Recurrent Neural Networks to reconstruct the transition probabilities matrix of the Markov sentiment process and recover the underlying sentiment states from the observed stock price behavior. We demonstrate that the Hidden Markov Model can successfully recover the transition probabilities matrix for the hidden sentiment process of the Markov Chain type. We also demonstrate that the Recurrent Neural Network can successfully recover the hidden sentiment states from the observed simulated stock price time series.

Economic and medical benefits of ultrasound screenings for gallstone disease.

PubMed

Shen, Hung-Ju; Hsu, Chung-Te; Tung, Tao-Hsin

2015-03-21

To investigate whether screening for gallstone disease was economically feasible and clinically effective. This clinical study was initially conducted in 2002 in Taipei, Taiwan. The study cohort total included 2386 healthy adults who were voluntarily admitted to a regional teaching hospital for a physical check-up. Annual follow-up screening with ultrasound sonography for gallstone disease continued until December 31, 2007. A decision analysis using the Markov Decision Model was constructed to compare different screening regimes for gallstone disease. The economic evaluation included estimates of both the cost-effectiveness and cost-utility of screening for gallstone disease. Direct costs included the cost of screening, regular clinical fees, laparoscopic cholecystectomy, and hospitalization. Indirect costs represent the loss of productivity attributable to the patient's disease state, and were estimated using the gross domestic product for 2011 in Taiwan. Longer time intervals in screening for gallstone disease were associated with the reduced efficacy and utility of screening and with increased cost. The cost per life-year gained (average cost-effectiveness ratio) for annual screening, biennial screening, 3-year screening, 4-year screening, 5-year screening, and no-screening was new Taiwan dollars (NTD) 39076, NTD 58059, NTD 72168, NTD 104488, NTD 126941, and NTD 197473, respectively (P < 0.05). The cost per quality-adjusted life-year gained by annual screening was NTD 40725; biennial screening, NTD 64868; 3-year screening, NTD 84532; 4-year screening, NTD 110962; 5-year screening, NTD 142053; and for the control group, NTD 202979 (P < 0.05). The threshold values indicated that the ultrasound sonography screening programs were highly sensitive to screening costs in a plausible range. Routine screening regime for gallstone disease is both medically and economically valuable. Annual screening for gallstone disease should be recommended.

Colorectal cancer screening comparing no screening, immunochemical and guaiac fecal occult blood tests: a cost-effectiveness analysis.

PubMed

van Rossum, Leo G M; van Rijn, Anne F; Verbeek, Andre L M; van Oijen, Martijn G H; Laheij, Robert J F; Fockens, Paul; Jansen, Jan B M J; Adang, Eddy M M; Dekker, Evelien

2011-04-15

Comparability of cost-effectiveness of colorectal cancer (CRC) screening strategies is limited if heterogeneous study data are combined. We analyzed prospective empirical data from a randomized-controlled trial to compare cost-effectiveness of screening with either one round of immunochemical fecal occult blood testing (I-FOBT; OC-Sensor®), one round of guaiac FOBT (G-FOBT; Hemoccult-II®) or no screening in Dutch aged 50 to 75 years, completed with cancer registry and literature data, from a third-party payer perspective in a Markov model with first- and second-order Monte Carlo simulation. Costs were measured in Euros (€), effects in life-years gained, and both were discounted with 3%. Uncertainty surrounding important parameters was analyzed. I-FOBT dominated the alternatives: after one round of I-FOBT screening, a hypothetical person would on average gain 0.003 life-years and save the health care system €27 compared with G-FOBT and 0.003 life years and €72 compared with no screening. Overall, in 4,460,265 Dutch aged 50-75 years, after one round I-FOBT screening, 13,400 life-years and €320 million would have been saved compared with no screening. I-FOBT also dominated in sensitivity analyses, varying uncertainty surrounding important effect and cost parameters. CRC screening with I-FOBT dominated G-FOBT and no screening with or without accounting for uncertainty. Copyright © 2010 UICC.

First and second order semi-Markov chains for wind speed modeling

NASA Astrophysics Data System (ADS)

Prattico, F.; Petroni, F.; D'Amico, G.

2012-04-01

The increasing interest in renewable energy leads scientific research to find a better way to recover most of the available energy. Particularly, the maximum energy recoverable from wind is equal to 59.3% of that available (Betz law) at a specific pitch angle and when the ratio between the wind speed in output and in input is equal to 1/3. The pitch angle is the angle formed between the airfoil of the blade of the wind turbine and the wind direction. Old turbine and a lot of that actually marketed, in fact, have always the same invariant geometry of the airfoil. This causes that wind turbines will work with an efficiency that is lower than 59.3%. New generation wind turbines, instead, have a system to variate the pitch angle by rotating the blades. This system able the wind turbines to recover, at different wind speed, always the maximum energy, working in Betz limit at different speed ratios. A powerful system control of the pitch angle allows the wind turbine to recover better the energy in transient regime. A good stochastic model for wind speed is then needed to help both the optimization of turbine design and to assist the system control to predict the value of the wind speed to positioning the blades quickly and correctly. The possibility to have synthetic data of wind speed is a powerful instrument to assist designer to verify the structures of the wind turbines or to estimate the energy recoverable from a specific site. To generate synthetic data, Markov chains of first or higher order are often used [1,2,3]. In particular in [3] is presented a comparison between a first-order Markov chain and a second-order Markov chain. A similar work, but only for the first-order Markov chain, is conduced by [2], presenting the probability transition matrix and comparing the energy spectral density and autocorrelation of real and synthetic wind speed data. A tentative to modeling and to join speed and direction of wind is presented in [1], by using two models, first-order Markov chain with different number of states, and Weibull distribution. All this model use Markov chains to generate synthetic wind speed time series but the search for a better model is still open. Approaching this issue, we applied new models which are generalization of Markov models. More precisely we applied semi-Markov models to generate synthetic wind speed time series. Semi-Markov processes (SMP) are a wide class of stochastic processes which generalize at the same time both Markov chains and renewal processes. Their main advantage is that of using whatever type of waiting time distribution for modeling the time to have a transition from one state to another one. This major flexibility has a price to pay: availability of data to estimate the parameters of the model which are more numerous. Data availability is not an issue in wind speed studies, therefore, semi-Markov models can be used in a statistical efficient way. In this work we present three different semi-Markov chain models: the first one is a first-order SMP where the transition probabilities from two speed states (at time Tn and Tn-1) depend on the initial state (the state at Tn-1), final state (the state at Tn) and on the waiting time (given by t=Tn-Tn-1), the second model is a second order SMP where we consider the transition probabilities as depending also on the state the wind speed was before the initial state (which is the state at Tn-2) and the last one is still a second order SMP where the transition probabilities depends on the three states at Tn-2,Tn-1 and Tn and on the waiting times t_1=Tn-1-Tn-2 and t_2=Tn-Tn-1. The three models are used to generate synthetic time series for wind speed by means of Monte Carlo simulations and the time lagged autocorrelation is used to compare statistical properties of the proposed models with those of real data and also with a time series generated though a simple Markov chain. [1] F. Youcef Ettoumi, H. Sauvageot, A.-E.-H. Adane, Statistical bivariate modeling of wind using first-order Markov chain and Weibull distribution, Renewable Energy, 28/2003 1787-1802. [2] A. Shamshad, M.A. Bawadi, W.M.W. Wan Hussin, T.A. Majid, S.A.M. Sanusi, First and second order Markov chain models for synthetic generation of wind speed time series, Energy 30/2005 693-708. [3] H. Nfaoui, H. Essiarab, A.A.M. Sayigh, A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco, Renewable Energy 29/2004, 1407-1418.

Computing rates of Markov models of voltage-gated ion channels by inverting partial differential equations governing the probability density functions of the conducting and non-conducting states.

PubMed

Tveito, Aslak; Lines, Glenn T; Edwards, Andrew G; McCulloch, Andrew

2016-07-01

Markov models are ubiquitously used to represent the function of single ion channels. However, solving the inverse problem to construct a Markov model of single channel dynamics from bilayer or patch-clamp recordings remains challenging, particularly for channels involving complex gating processes. Methods for solving the inverse problem are generally based on data from voltage clamp measurements. Here, we describe an alternative approach to this problem based on measurements of voltage traces. The voltage traces define probability density functions of the functional states of an ion channel. These probability density functions can also be computed by solving a deterministic system of partial differential equations. The inversion is based on tuning the rates of the Markov models used in the deterministic system of partial differential equations such that the solution mimics the properties of the probability density function gathered from (pseudo) experimental data as well as possible. The optimization is done by defining a cost function to measure the difference between the deterministic solution and the solution based on experimental data. By evoking the properties of this function, it is possible to infer whether the rates of the Markov model are identifiable by our method. We present applications to Markov model well-known from the literature. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Markov Chain Estimation of Avian Seasonal Fecundity

EPA Science Inventory

To explore the consequences of modeling decisions on inference about avian seasonal fecundity we generalize previous Markov chain (MC) models of avian nest success to formulate two different MC models of avian seasonal fecundity that represent two different ways to model renestin...

Nonparametric model validations for hidden Markov models with applications in financial econometrics

PubMed Central

Zhao, Zhibiao

2011-01-01

We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise. PMID:21750601

VAMPnets for deep learning of molecular kinetics.

PubMed

Mardt, Andreas; Pasquali, Luca; Wu, Hao; Noé, Frank

2018-01-02

There is an increasing demand for computing the relevant structures, equilibria, and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput molecular dynamics simulations. Current methods employ transformation of simulated coordinates into structural features, dimension reduction, clustering the dimension-reduced data, and estimation of a Markov state model or related model of the interconversion rates between molecular structures. This handcrafted approach demands a substantial amount of modeling expertise, as poor decisions at any step will lead to large modeling errors. Here we employ the variational approach for Markov processes (VAMP) to develop a deep learning framework for molecular kinetics using neural networks, dubbed VAMPnets. A VAMPnet encodes the entire mapping from molecular coordinates to Markov states, thus combining the whole data processing pipeline in a single end-to-end framework. Our method performs equally or better than state-of-the-art Markov modeling methods and provides easily interpretable few-state kinetic models.

Illustrating economic evaluation of diagnostic technologies: comparing Helicobacter pylori screening strategies in prevention of gastric cancer in Canada.

PubMed

Xie, Feng; O'Reilly, Daria; Ferrusi, Ilia L; Blackhouse, Gord; Bowen, James M; Tarride, Jean-Eric; Goeree, Ron

2009-05-01

The aim of this paper is to present an economic evaluation of diagnostic technologies using Helicobacter pylori screening strategies for the prevention of gastric cancer as an illustration. A Markov model was constructed to compare the lifetime cost and effectiveness of 4 potential strategies: no screening, the serology test by enzyme-linked immunosorbent assay (ELISA), the stool antigen test (SAT), and the (13)C-urea breath test (UBT) for the detection of H. pylori among a hypothetical cohort of 10,000 Canadian men aged 35 years. Special parameter consideration included the sensitivity and specificity of each screening strategy, which determined the model structure and treatment regimen. The primary outcome measured was the incremental cost-effectiveness ratio between the screening strategies and the no-screening strategy. Base-case analysis and probabilistic sensitivity analysis were performed using the point estimates of the parameters and Monte Carlo simulations, respectively. Compared with the no-screening strategy in the base-case analysis, the incremental cost-effectiveness ratio was $33,000 per quality-adjusted life-year (QALY) for the ELISA, $29,800 per QALY for the SAT, and $50,400 per QALY for the UBT. The probabilistic sensitivity analysis revealed that the no-screening strategy was more cost effective if the willingness to pay (WTP) was <$20,000 per QALY, while the SAT had the highest probability of being cost effective if the WTP was >$30,000 per QALY. Both the ELISA and the UBT were not cost-effective strategies over a wide range of WTP values. Although the UBT had the highest sensitivity and specificity, either no screening or the SAT could be the most cost-effective strategy depending on the WTP threshold values from an economic perspective. This highlights the importance of economic evaluations of diagnostic technologies.

Cost-effectiveness analysis of ultrasonography screening for nonalcoholic fatty liver disease in metabolic syndrome patients.

PubMed

Phisalprapa, Pochamana; Supakankunti, Siripen; Charatcharoenwitthaya, Phunchai; Apisarnthanarak, Piyaporn; Charoensak, Aphinya; Washirasaksiri, Chaiwat; Srivanichakorn, Weerachai; Chaiyakunapruk, Nathorn

2017-04-01

Nonalcoholic fatty liver disease (NAFLD) can be diagnosed early by noninvasive ultrasonography; however, the cost-effectiveness of ultrasonography screening with intensive weight reduction program in metabolic syndrome patients is not clear. This study aims to estimate economic and clinical outcomes of ultrasonography in Thailand. Cost-effectiveness analysis used decision tree and Markov models to estimate lifetime costs and health benefits from societal perspective, based on a cohort of 509 metabolic syndrome patients in Thailand. Data were obtained from published literatures and Thai database. Results were reported as incremental cost-effectiveness ratios (ICERs) in 2014 US dollars (USD) per quality-adjusted life year (QALY) gained with discount rate of 3%. Sensitivity analyses were performed to assess the influence of parameter uncertainty on the results. The ICER of ultrasonography screening of 50-year-old metabolic syndrome patients with intensive weight reduction program was 958 USD/QALY gained when compared with no screening. The probability of being cost-effective was 67% using willingness-to-pay threshold in Thailand (4848 USD/QALY gained). Screening before 45 years was cost saving while screening at 45 to 64 years was cost-effective. For patients with metabolic syndromes, ultrasonography screening for NAFLD with intensive weight reduction program is a cost-effective program in Thailand. Study can be used as part of evidence-informed decision making. Findings could contribute to changes of NAFLD diagnosis practice in settings where economic evidence is used as part of decision-making process. Furthermore, study design, model structure, and input parameters could also be used for future research addressing similar questions.

Cost-effectiveness of Colorectal Cancer Screening and Treatment Methods: Mapping of Systematic Reviews

PubMed Central

Abdolahi, Hossein Mashhadi; Asiabar, Ali Sarabi; Azami-Aghdash, Saber; Pournaghi-Azar, Fatemeh; Rezapour, Aziz

2018-01-01

Objective: Due to extensive literature on colorectal cancer and their heterogeneous results, this study aimed to summarize the systematic reviews which review the cost-effectiveness studies on different aspects of colorectal cancer. Methods: The required data were collected by searching the following key words according to MeSH: “colorectal cancer,” “colorectal oncology,” “colorectal carcinoma,” “colorectal neoplasm,” “colorectal tumors,” “cost-effectiveness,” “systematic review,” and “meta-analysis.” The following databases were searched: PubMed, Cochrane, Google Scholar, and Scopus. Two reviewers evaluated the articles according to the checklist of “assessment of multiple systematic reviews” (AMSTAR) tool. Results: Finally, eight systematic reviews were included in the study. The Drummond checklist was mostly used for assessing the quality of the articles. The main perspective was related to the payer and the least was relevant to the social. The majority of the cases referred to sensitivity analysis (in 76% of the cases) and the lowest point also was allocated to discounting (in 37% of cases). The Markov model was used most widely in the studies. Treatment methods examined in the studies were not cost-effective in comparison with the studied units. Among the screening methods, computerized tomographic colonography and fecal DNA were cost-effective. The average score of the articles’ qualities was high (9.8 out of 11). Conclusions: The community perspective should be taken into consideration at large in the studies. It is necessary to pay more attention to discounting subject in studies. More frequent application of the Markov model is recommended. PMID:29379836

A reward semi-Markov process with memory for wind speed modeling

NASA Astrophysics Data System (ADS)

Petroni, F.; D'Amico, G.; Prattico, F.

2012-04-01

The increasing interest in renewable energy leads scientific research to find a better way to recover most of the available energy. Particularly, the maximum energy recoverable from wind is equal to 59.3% of that available (Betz law) at a specific pitch angle and when the ratio between the wind speed in output and in input is equal to 1/3. The pitch angle is the angle formed between the airfoil of the blade of the wind turbine and the wind direction. Old turbine and a lot of that actually marketed, in fact, have always the same invariant geometry of the airfoil. This causes that wind turbines will work with an efficiency that is lower than 59.3%. New generation wind turbines, instead, have a system to variate the pitch angle by rotating the blades. This system able the wind turbines to recover, at different wind speed, always the maximum energy, working in Betz limit at different speed ratios. A powerful system control of the pitch angle allows the wind turbine to recover better the energy in transient regime. A good stochastic model for wind speed is then needed to help both the optimization of turbine design and to assist the system control to predict the value of the wind speed to positioning the blades quickly and correctly. The possibility to have synthetic data of wind speed is a powerful instrument to assist designer to verify the structures of the wind turbines or to estimate the energy recoverable from a specific site. To generate synthetic data, Markov chains of first or higher order are often used [1,2,3]. In particular in [1] is presented a comparison between a first-order Markov chain and a second-order Markov chain. A similar work, but only for the first-order Markov chain, is conduced by [2], presenting the probability transition matrix and comparing the energy spectral density and autocorrelation of real and synthetic wind speed data. A tentative to modeling and to join speed and direction of wind is presented in [3], by using two models, first-order Markov chain with different number of states, and Weibull distribution. All this model use Markov chains to generate synthetic wind speed time series but the search for a better model is still open. Approaching this issue, we applied new models which are generalization of Markov models. More precisely we applied semi-Markov models to generate synthetic wind speed time series. The primary goal of this analysis is the study of the time history of the wind in order to assess its reliability as a source of power and to determine the associated storage levels required. In order to assess this issue we use a probabilistic model based on indexed semi-Markov process [4] to which a reward structure is attached. Our model is used to calculate the expected energy produced by a given turbine and its variability expressed by the variance of the process. Our results can be used to compare different wind farms based on their reward and also on the risk of missed production due to the intrinsic variability of the wind speed process. The model is used to generate synthetic time series for wind speed by means of Monte Carlo simulations and backtesting procedure is used to compare results on first and second oder moments of rewards between real and synthetic data. [1] A. Shamshad, M.A. Bawadi, W.M.W. Wan Hussin, T.A. Majid, S.A.M. Sanusi, First and second order Markov chain models for synthetic gen- eration of wind speed time series, Energy 30 (2005) 693-708. [2] H. Nfaoui, H. Essiarab, A.A.M. Sayigh, A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco, Re- newable Energy 29 (2004) 1407-1418. [3] F. Youcef Ettoumi, H. Sauvageot, A.-E.-H. Adane, Statistical bivariate modeling of wind using first-order Markov chain and Weibull distribu- tion, Renewable Energy 28 (2003) 1787-1802. [4]F. Petroni, G. D'Amico, F. Prattico, Indexed semi-Markov process for wind speed modeling. To be submitted.

Health economic analysis of human papillomavirus vaccines in women of Chile: perspective of the health care payer using a Markov model.

PubMed

Gomez, Jorge Alberto; Lepetic, Alejandro; Demarteau, Nadia

2014-11-26

In Chile, significant reductions in cervical cancer incidence and mortality have been observed due to implementation of a well-organized screening program. However, it has been suggested that the inclusion of human papillomavirus (HPV) vaccination for young adolescent women may be the best prospect to further reduce the burden of cervical cancer. This cost-effectiveness study comparing two available HPV vaccines in Chile was performed to support decision making on the implementation of universal HPV vaccination. The present analysis used an existing static Markov model to assess the effect of screening and vaccination. This analysis includes the epidemiology of low-risk HPV types allowing for the comparison between the two vaccines (HPV-16/18 AS04-adjuvanted vaccine and the HPV-6/11/16/18 vaccine), latest cross-protection data on HPV vaccines, treatment costs for cervical cancer, vaccine costs and 6% discounting per the health economic guideline for Chile. Projected incremental cost-utility ratio (ICUR) and incremental cost-effectiveness ratio (ICERs) for the HPV-16/18 AS04-adjuvanted vaccine was 116 United States (US) dollars per quality-adjusted life years (QALY) gained or 147 US dollars per life-years (LY) saved, while the projected ICUR/ICER for the HPV-6/11/16/18 vaccine was 541 US dollars per QALY gained or 726 US dollars per LY saved. Introduction of any HPV vaccine to the present cervical cancer prevention program of Chile is estimated to be highly cost-effective (below 1X gross domestic product [GDP] per capita, 14278 US dollars). In Chile, the addition of HPV-16/18 AS04-adjuvanted vaccine to the existing screening program dominated the addition of HPV-6/11/16/18 vaccine. In the probabilistic sensitivity analysis results show that the HPV-16/18 AS04-adjuvanted vaccine is expected to be dominant and cost-saving in 69.3% and 77.6% of the replicates respectively. The findings indicate that the addition of any HPV vaccine to the current cervical screening program of Chile will be advantageous. However, this cost-effectiveness model shows that the HPV-16/18 AS04-adjuvanted vaccine dominated the HPV-6/11/16/18 vaccine. Beyond the context of Chile, the data from this modelling exercise may support healthcare policy and decision-making pertaining to introduction of HPV vaccination in similar resource settings in the region.

Bayesian analysis of non-homogeneous Markov chains: application to mental health data.

PubMed

Sung, Minje; Soyer, Refik; Nhan, Nguyen

2007-07-10

In this paper we present a formal treatment of non-homogeneous Markov chains by introducing a hierarchical Bayesian framework. Our work is motivated by the analysis of correlated categorical data which arise in assessment of psychiatric treatment programs. In our development, we introduce a Markovian structure to describe the non-homogeneity of transition patterns. In doing so, we introduce a logistic regression set-up for Markov chains and incorporate covariates in our model. We present a Bayesian model using Markov chain Monte Carlo methods and develop inference procedures to address issues encountered in the analyses of data from psychiatric treatment programs. Our model and inference procedures are implemented to some real data from a psychiatric treatment study. Copyright 2006 John Wiley & Sons, Ltd.

Validation of the SURE Program, phase 1

NASA Technical Reports Server (NTRS)

Dotson, Kelly J.

1987-01-01

Presented are the results of the first phase in the validation of the SURE (Semi-Markov Unreliability Range Evaluator) program. The SURE program gives lower and upper bounds on the death-state probabilities of a semi-Markov model. With these bounds, the reliability of a semi-Markov model of a fault-tolerant computer system can be analyzed. For the first phase in the validation, fifteen semi-Markov models were solved analytically for the exact death-state probabilities and these solutions compared to the corresponding bounds given by SURE. In every case, the SURE bounds covered the exact solution. The bounds, however, had a tendency to separate in cases where the recovery rate was slow or the fault arrival rate was fast.

Influence of credit scoring on the dynamics of Markov chain

NASA Astrophysics Data System (ADS)

Galina, Timofeeva

2015-11-01

Markov processes are widely used to model the dynamics of a credit portfolio and forecast the portfolio risk and profitability. In the Markov chain model the loan portfolio is divided into several groups with different quality, which determined by presence of indebtedness and its terms. It is proposed that dynamics of portfolio shares is described by a multistage controlled system. The article outlines mathematical formalization of controls which reflect the actions of the bank's management in order to improve the loan portfolio quality. The most important control is the organization of approval procedure of loan applications. The credit scoring is studied as a control affecting to the dynamic system. Different formalizations of "good" and "bad" consumers are proposed in connection with the Markov chain model.

Zero-state Markov switching count-data models: an empirical assessment.

PubMed

Malyshkina, Nataliya V; Mannering, Fred L

2010-01-01

In this study, a two-state Markov switching count-data model is proposed as an alternative to zero-inflated models to account for the preponderance of zeros sometimes observed in transportation count data, such as the number of accidents occurring on a roadway segment over some period of time. For this accident-frequency case, zero-inflated models assume the existence of two states: one of the states is a zero-accident count state, which has accident probabilities that are so low that they cannot be statistically distinguished from zero, and the other state is a normal-count state, in which counts can be non-negative integers that are generated by some counting process, for example, a Poisson or negative binomial. While zero-inflated models have come under some criticism with regard to accident-frequency applications - one fact is undeniable - in many applications they provide a statistically superior fit to the data. The Markov switching approach we propose seeks to overcome some of the criticism associated with the zero-accident state of the zero-inflated model by allowing individual roadway segments to switch between zero and normal-count states over time. An important advantage of this Markov switching approach is that it allows for the direct statistical estimation of the specific roadway-segment state (i.e., zero-accident or normal-count state) whereas traditional zero-inflated models do not. To demonstrate the applicability of this approach, a two-state Markov switching negative binomial model (estimated with Bayesian inference) and standard zero-inflated negative binomial models are estimated using five-year accident frequencies on Indiana interstate highway segments. It is shown that the Markov switching model is a viable alternative and results in a superior statistical fit relative to the zero-inflated models.

Discrete Latent Markov Models for Normally Distributed Response Data

ERIC Educational Resources Information Center

Schmittmann, Verena D.; Dolan, Conor V.; van der Maas, Han L. J.; Neale, Michael C.

2005-01-01

Van de Pol and Langeheine (1990) presented a general framework for Markov modeling of repeatedly measured discrete data. We discuss analogical single indicator models for normally distributed responses. In contrast to discrete models, which have been studied extensively, analogical continuous response models have hardly been considered. These…

A simplified parsimonious higher order multivariate Markov chain model

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, a simplified parsimonious higher-order multivariate Markov chain model (SPHOMMCM) is presented. Moreover, parameter estimation method of TPHOMMCM is give. Numerical experiments shows the effectiveness of TPHOMMCM.

A tridiagonal parsimonious higher order multivariate Markov chain model

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, we present a tridiagonal parsimonious higher-order multivariate Markov chain model (TPHOMMCM). Moreover, estimation method of the parameters in TPHOMMCM is give. Numerical experiments illustrate the effectiveness of TPHOMMCM.

Hideen Markov Models and Neural Networks for Fault Detection in Dynamic Systems

NASA Technical Reports Server (NTRS)

Smyth, Padhraic

1994-01-01

None given. (From conclusion): Neural networks plus Hidden Markov Models(HMM)can provide excellene detection and false alarm rate performance in fault detection applications. Modified models allow for novelty detection. Also covers some key contributions of neural network model, and application status.

Effect of Clustering Algorithm on Establishing Markov State Model for Molecular Dynamics Simulations.

PubMed

Li, Yan; Dong, Zigang

2016-06-27

Recently, the Markov state model has been applied for kinetic analysis of molecular dynamics simulations. However, discretization of the conformational space remains a primary challenge in model building, and it is not clear how the space decomposition by distinct clustering strategies exerts influence on the model output. In this work, different clustering algorithms are employed to partition the conformational space sampled in opening and closing of fatty acid binding protein 4 as well as inactivation and activation of the epidermal growth factor receptor. Various classifications are achieved, and Markov models are set up accordingly. On the basis of the models, the total net flux and transition rate are calculated between two distinct states. Our results indicate that geometric and kinetic clustering perform equally well. The construction and outcome of Markov models are heavily dependent on the data traits. Compared to other methods, a combination of Bayesian and hierarchical clustering is feasible in identification of metastable states.

Cost-effectiveness of screening for abdominal aortic aneurysm in the Netherlands and Norway.

PubMed

Spronk, S; van Kempen, B J H; Boll, A P M; Jørgensen, J J; Hunink, M G M; Kristiansen, I S

2011-11-01

The aim of this study was to determine the cost-effectiveness of ultrasound screening for abdominal aortic aneurysm (AAA) in men aged 65 years, for both the Netherlands and Norway. A Markov model was developed to simulate life expectancy, quality-adjusted life-years, net health benefits, lifetime costs and incremental cost-effectiveness ratios for both screening and no screening for AAA. The best available evidence was retrieved from the literature and combined with primary data from the two countries separately, and analysed from a national perspective. A threshold willingness-to-pay (WTP) of €20,000 and €62,500 was used for data from the Netherlands and Norway respectively. The additional costs of the screening strategy compared with no screening were €421 (95 per cent confidence interval 33 to 806) per person in the Netherlands, and the additional life-years were 0·097 (-0·180 to 0·365), representing €4340 per life-year. For Norway, the values were €562 (59 to 1078), 0·057 (-0·135 to 0·253) life-years and €9860 per life-year respectively. In Norway the results were sensitive to a decrease in the prevalence of AAA in 65-year-old men to 1 per cent, or lower. Probabilistic sensitivity analyses indicated that AAA screening has a 70 per cent probability of being cost-effective in the Netherlands with a WTP threshold of €20,000, and 70 per cent in Norway with a threshold of €62,500. Using this model, screening for AAA in 65-year-old men would be highly cost-effective in both the Netherlands and Norway. Copyright © 2011 British Journal of Surgery Society Ltd. Published by John Wiley & Sons, Ltd.

Invited commentary: Lost in estimation--searching for alternatives to markov chains to fit complex Bayesian models.

PubMed

Molitor, John

2012-03-01

Bayesian methods have seen an increase in popularity in a wide variety of scientific fields, including epidemiology. One of the main reasons for their widespread application is the power of the Markov chain Monte Carlo (MCMC) techniques generally used to fit these models. As a result, researchers often implicitly associate Bayesian models with MCMC estimation procedures. However, Bayesian models do not always require Markov-chain-based methods for parameter estimation. This is important, as MCMC estimation methods, while generally quite powerful, are complex and computationally expensive and suffer from convergence problems related to the manner in which they generate correlated samples used to estimate probability distributions for parameters of interest. In this issue of the Journal, Cole et al. (Am J Epidemiol. 2012;175(5):368-375) present an interesting paper that discusses non-Markov-chain-based approaches to fitting Bayesian models. These methods, though limited, can overcome some of the problems associated with MCMC techniques and promise to provide simpler approaches to fitting Bayesian models. Applied researchers will find these estimation approaches intuitively appealing and will gain a deeper understanding of Bayesian models through their use. However, readers should be aware that other non-Markov-chain-based methods are currently in active development and have been widely published in other fields.

CellCognition: time-resolved phenotype annotation in high-throughput live cell imaging.

PubMed

Held, Michael; Schmitz, Michael H A; Fischer, Bernd; Walter, Thomas; Neumann, Beate; Olma, Michael H; Peter, Matthias; Ellenberg, Jan; Gerlich, Daniel W

2010-09-01

Fluorescence time-lapse imaging has become a powerful tool to investigate complex dynamic processes such as cell division or intracellular trafficking. Automated microscopes generate time-resolved imaging data at high throughput, yet tools for quantification of large-scale movie data are largely missing. Here we present CellCognition, a computational framework to annotate complex cellular dynamics. We developed a machine-learning method that combines state-of-the-art classification with hidden Markov modeling for annotation of the progression through morphologically distinct biological states. Incorporation of time information into the annotation scheme was essential to suppress classification noise at state transitions and confusion between different functional states with similar morphology. We demonstrate generic applicability in different assays and perturbation conditions, including a candidate-based RNA interference screen for regulators of mitotic exit in human cells. CellCognition is published as open source software, enabling live-cell imaging-based screening with assays that directly score cellular dynamics.

Tracking Skill Acquisition with Cognitive Diagnosis Models: A Higher-Order, Hidden Markov Model with Covariates

ERIC Educational Resources Information Center

Wang, Shiyu; Yang, Yan; Culpepper, Steven Andrew; Douglas, Jeffrey A.

2018-01-01

A family of learning models that integrates a cognitive diagnostic model and a higher-order, hidden Markov model in one framework is proposed. This new framework includes covariates to model skill transition in the learning environment. A Bayesian formulation is adopted to estimate parameters from a learning model. The developed methods are…

Markov chain model for demersal fish catch analysis in Indonesia

NASA Astrophysics Data System (ADS)

Firdaniza; Gusriani, N.

2018-03-01

As an archipelagic country, Indonesia has considerable potential fishery resources. One of the fish resources that has high economic value is demersal fish. Demersal fish is a fish with a habitat in the muddy seabed. Demersal fish scattered throughout the Indonesian seas. Demersal fish production in each Indonesia’s Fisheries Management Area (FMA) varies each year. In this paper we have discussed the Markov chain model for demersal fish yield analysis throughout all Indonesia’s Fisheries Management Area. Data of demersal fish catch in every FMA in 2005-2014 was obtained from Directorate of Capture Fisheries. From this data a transition probability matrix is determined by the number of transitions from the catch that lie below the median or above the median. The Markov chain model of demersal fish catch data was an ergodic Markov chain model, so that the limiting probability of the Markov chain model can be determined. The predictive value of demersal fishing yields was obtained by calculating the combination of limiting probability with average catch results below the median and above the median. The results showed that for 2018 and long-term demersal fishing results in most of FMA were below the median value.

Cost-effectiveness of HPV vaccination in the context of high cervical cancer incidence and low screening coverage.

PubMed

Võrno, Triin; Lutsar, Katrin; Uusküla, Anneli; Padrik, Lee; Raud, Terje; Reile, Rainer; Nahkur, Oliver; Kiivet, Raul-Allan

2017-11-01

Estonia has high cervical cancer incidence and low screening coverage. We modelled the impact of population-based bivalent, quadrivalent or nonavalent HPV vaccination alongside cervical cancer screening. A Markov cohort model of the natural history of HPV infection was used to assess the cost-effectiveness of vaccinating a cohort of 12-year-old girls with bivalent, quadrivalent or nonavalent vaccine in two doses in a national, school-based vaccination programme. The model followed the natural progression of HPV infection into subsequent genital warts (GW); premalignant lesions (CIN1-3); cervical, oropharyngeal, vulvar, vaginal and anal cancer. Vaccine coverage was assumed to be 70%. A time horizon of 88years (up to 100years of age) was used to capture all lifetime vaccination costs and benefits. Costs and utilities were discounted using an annual discount rate of 5%. Vaccination of 12-year-old girls alongside screening compared to screening alone had an incremental cost-effectiveness ratio (ICER) of €14,007 (bivalent), €14,067 (quadrivalent) and €11,633 (nonavalent) per quality-adjusted life-year (QALY) in the base-case scenario and ranged between €5367-21,711, €5142-21,800 and €4563-18,142, respectively, in sensitivity analysis. The results were most sensitive to changes in discount rate, vaccination regimen, vaccine prices and cervical cancer screening coverage. Vaccination of 12-year-old girls alongside current cervical cancer screening can be considered a cost-effective intervention in Estonia. Adding HPV vaccination to the national immunisation schedule is expected to prevent a considerable number of HPV infections, genital warts, premalignant lesions, HPV related cancers and deaths. Although in our model ICERs varied slightly depending on the vaccine used, they generally fell within the same range. Cost-effectiveness of HPV vaccination was found to be most dependent on vaccine cost and duration of vaccine immunity, but not on the type of vaccine used. Copyright © 2017 Elsevier Ltd. All rights reserved.

Optimizing screening for tuberculosis and hepatitis B prior to starting tumor necrosis factor-α inhibitors in Crohn's disease.

PubMed

van der Have, Mike; Oldenburg, Bas; Fidder, Herma H; Belderbos, Tim D G; Siersema, Peter D; van Oijen, Martijn G H

2014-03-01

Treatment with tumor necrosis factor-α (TNF-α) inhibitors in patients with Crohn's disease (CD) is associated with potentially serious infections, including tuberculosis (TB) and hepatitis B virus (HBV). We assessed the cost-effectiveness of extensive TB screening and HBV screening prior to initiating TNF-α inhibitors in CD. We constructed two Markov models: (1) comparing tuberculin skin test (TST) combined with chest X-ray (conventional TB screening) versus TST and chest X-ray followed by the interferon-gamma release assay (extensive TB screening) in diagnosing TB; and (2) HBV screening versus no HBV screening. Our base-case included an adult CD patient starting with infliximab treatment. Input parameters were extracted from the literature. Direct medical costs were assessed and discounted following a third-party payer perspective. The main outcome was the incremental cost-effectiveness ratio (ICER). Sensitivity and Monte Carlo analyses were performed over wide ranges of probability and cost estimates. At base-case, the ICERs of extensive screening and HBV screening were €64,340 and €75,760 respectively to gain one quality-adjusted life year. Sensitivity analyses concluded that extensive TB screening was a cost-effective strategy if the latent TB prevalence is more than 12 % or if the false positivity rate of TST is more than 20 %. HBV screening became cost-effective if HBV reactivation or HBV-related mortality is higher than 37 and 62 %, respectively. Extensive TB screening and HBV screening are not cost-effective compared with conventional TB screening and no HBV screening, respectively. However, when targeted at high-risk patient groups, these screening strategies are likely to become cost-effective.

Two Aspects of the Simplex Model: Goodness of Fit to Linear Growth Curve Structures and the Analysis of Mean Trends.

ERIC Educational Resources Information Center

Mandys, Frantisek; Dolan, Conor V.; Molenaar, Peter C. M.

1994-01-01

Studied the conditions under which the quasi-Markov simplex model fits a linear growth curve covariance structure and determined when the model is rejected. Presents a quasi-Markov simplex model with structured means and gives an example. (SLD)

Population Screening for Hereditary Haemochromatosis in Australia: Construction and Validation of a State-Transition Cost-Effectiveness Model.

PubMed

de Graaff, Barbara; Si, Lei; Neil, Amanda L; Yee, Kwang Chien; Sanderson, Kristy; Gurrin, Lyle C; Palmer, Andrew J

2017-03-01

HFE-associated haemochromatosis, the most common monogenic disorder amongst populations of northern European ancestry, is characterised by iron overload. Excess iron is stored in parenchymal tissues, leading to morbidity and mortality. Population screening programmes are likely to improve early diagnosis, thereby decreasing associated disease. Our aim was to develop and validate a health economics model of screening using utilities and costs from a haemochromatosis cohort. A state-transition model was developed with Markov states based on disease severity. Australian males (aged 30 years) and females (aged 45 years) of northern European ancestry were the target populations. The screening strategy was the status quo approach in Australia; the model was run over a lifetime horizon. Costs were estimated from the government perspective and reported in 2015 Australian dollars ($A); costs and quality-adjusted life-years (QALYs) were discounted at 5% annually. Model validity was assessed using goodness-of-fit analyses. Second-order Monte-Carlo simulation was used to account for uncertainty in multiple parameters. For validity, the model reproduced mortality, life expectancy (LE) and prevalence rates in line with published data. LE for C282Y homozygote males and females were 49.9 and 40.2 years, respectively, slightly lower than population rates. Mean (95% confidence interval) QALYS were 15.7 (7.7-23.7) for males and 14.4 (6.7-22.1) for females. Mean discounted lifetime costs for C282Y homozygotes were $A22,737 (3670-85,793) for males and $A13,840 (1335-67,377) for females. Sensitivity analyses revealed discount rates and prevalence had the greatest impacts on outcomes. We have developed a transparent, validated health economics model of C282Y homozygote haemochromatosis. The model will be useful to decision makers to identify cost-effective screening strategies.

"To screen or not to screen": Comparing the health and economic benefits of early peanut introduction strategies in five countries.

PubMed

Shaker, M; Stukus, D; Chan, E S; Fleischer, D M; Spergel, J M; Greenhawt, M

2018-03-30

Early peanut introduction (EPI) in the first year of life is associated with reduced risk of developing peanut allergy in children with either severe eczema and/or egg allergy. However, EPI recommendations differ among countries with formal guidelines. Using simulation and Markov modeling over a 20-year horizon to attempt to explore optimal EPI strategies applied to the US population, we compared high-risk infant-specific IgE peanut screening (US/Canadian) with the Australiasian Society for Clinical Immunology and Allergy (Australia/New Zealand) (ASCIA) and the United Kingdom Department of Health (UKDOH)-published EPI approaches. Screening peanut skin testing of all children with early-onset eczema and/or egg allergy before in-office peanut introduction was dominated by a no screening approach, in terms of number of cases of peanut allergy prevented, quality-adjusted life years (QALY), and healthcare costs, although screening resulted in a slightly lower rate of allergic reactions to peanut per patient in high-risk children. Considering costs of peanut allergy in high-risk children, the per-patient cost of early introduction without screening over the model horizon was $6556.69 (95%CI, $6512.76-$6600.62), compared with a cost of $7576.32 (95%CI, $7531.38-$7621.26) for skin test screening prior to introduction. From a US societal perspective, screening prior to introduction cost $654 115 322 and resulted in 3208 additional peanut allergy diagnoses. Both screening and nonscreening approaches dominated deliberately delayed peanut introduction. A no-screening approach for EPI has superior health and economic benefits in terms of number of peanut allergy cases prevented, QALY, and total healthcare costs compared to screening and in-office peanut introduction. © 2018 EAACI and John Wiley and Sons A/S. Published by John Wiley and Sons Ltd.

Computed tomographic colonography to screen for colorectal cancer, extracolonic cancer, and aortic aneurysm: model simulation with cost-effectiveness analysis.

PubMed

Hassan, Cesare; Pickhardt, Perry J; Pickhardt, Perry; Laghi, Andrea; Kim, Daniel H; Kim, Daniel; Zullo, Angelo; Iafrate, Franco; Di Giulio, Lorenzo; Morini, Sergio

2008-04-14

In addition to detecting colorectal neoplasia, abdominal computed tomography (CT) with colonography technique (CTC) can also detect unsuspected extracolonic cancers and abdominal aortic aneurysms (AAA).The efficacy and cost-effectiveness of this combined abdominal CT screening strategy are unknown. A computerized Markov model was constructed to simulate the occurrence of colorectal neoplasia, extracolonic malignant neoplasm, and AAA in a hypothetical cohort of 100,000 subjects from the United States who were 50 years of age. Simulated screening with CTC, using a 6-mm polyp size threshold for reporting, was compared with a competing model of optical colonoscopy (OC), both without and with abdominal ultrasonography for AAA detection (OC-US strategy). In the simulated population, CTC was the dominant screening strategy, gaining an additional 1458 and 462 life-years compared with the OC and OC-US strategies and being less costly, with a savings of $266 and $449 per person, respectively. The additional gains for CTC were largely due to a decrease in AAA-related deaths, whereas the modeled benefit from extracolonic cancer downstaging was a relatively minor factor. At sensitivity analysis, OC-US became more cost-effective only when the CTC sensitivity for large polyps dropped to 61% or when broad variations of costs were simulated, such as an increase in CTC cost from $814 to $1300 or a decrease in OC cost from $1100 to $500. With the OC-US approach, suboptimal compliance had a strong negative influence on efficacy and cost-effectiveness. The estimated mortality from CT-induced cancer was less than estimated colonoscopy-related mortality (8 vs 22 deaths), both of which were minor compared with the positive benefit from screening. When detection of extracolonic findings such as AAA and extracolonic cancer are considered in addition to colorectal neoplasia in our model simulation, CT colonography is a dominant screening strategy (ie, more clinically effective and more cost-effective) over both colonoscopy and colonoscopy with 1-time ultrasonography.

Using Simulation to Model and Validate Invasive Breast Cancer Progression in Women in the Study and Control Groups of the Canadian National Breast Screening Studies I and II.

PubMed

Taghipour, Sharareh; Caudrelier, Laurent N; Miller, Anthony B; Harvey, Bart

2017-02-01

Modeling breast cancer progression and the effect of various risk is helpful in deciding when a woman should start and end screening, and how often the screening should be undertaken. We modeled the natural progression of breast cancer using a hidden Markov process, and incorporated the effects of covariates. Patients are women aged 50-59 (older) and 40-49 (younger) years from the Canadian National Breast Screening Studies. We included prevalent cancers, estimated the screening sensitivities and rates of over-diagnosis, and validated the models using simulation. We found that older women have a higher rate of transition from a healthy to preclinical state and other causes of death but a lower rate of transition from preclinical to clinical state. Reciprocally, younger women have a lower rate of transition from a healthy to preclinical state and other causes of death but a higher rate of transition from a preclinical to clinical state. Different risk factors were significant for the age groups. The mean sojourn times for older and younger women were 2.53 and 2.96 years, respectively. In the study group, the sensitivities of the initial physical examination and mammography for older and younger women were 0.87 and 0.81, respectively, and the sensitivity of the subsequent screens were 0.78 and 0.53, respectively. In the control groups, the sensitivities of the initial physical examination for older and younger women were 0.769 and 0.671, respectively, and the sensitivity of the subsequent physical examinations for the control group aged 50-59 years was 0.37. The upper-bounds for over-diagnosis in older and younger women were 25% and 27%, respectively. The present work offers a basis for the better modeling of cancer incidence for a population with the inclusion of prevalent cancers.

Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression

PubMed Central

Liu, Yu-Ying; Li, Shuang; Li, Fuxin; Song, Le; Rehg, James M.

2016-01-01

The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in time. However, the lack of an efficient parameter learning algorithm for CT-HMM restricts its use to very small models or requires unrealistic constraints on the state transitions. In this paper, we present the first complete characterization of efficient EM-based learning methods for CT-HMM models. We demonstrate that the learning problem consists of two challenges: the estimation of posterior state probabilities and the computation of end-state conditioned statistics. We solve the first challenge by reformulating the estimation problem in terms of an equivalent discrete time-inhomogeneous hidden Markov model. The second challenge is addressed by adapting three approaches from the continuous time Markov chain literature to the CT-HMM domain. We demonstrate the use of CT-HMMs with more than 100 states to visualize and predict disease progression using a glaucoma dataset and an Alzheimer’s disease dataset. PMID:27019571

Visual screening for malignant melanoma: a cost-effectiveness analysis.

PubMed

Losina, Elena; Walensky, Rochelle P; Geller, Alan; Beddingfield, Frederick C; Wolf, Lindsey L; Gilchrest, Barbara A; Freedberg, Kenneth A

2007-01-01

To evaluate the cost-effectiveness of various melanoma screening strategies proposed in the United States. We developed a computer simulation Markov model to evaluate alternative melanoma screening strategies. Hypothetical cohort of the general population and siblings of patients with melanoma. Intervention We considered the following 4 strategies: background screening only, and screening 1 time, every 2 years, and annually, all beginning at age 50 years. Prevalence, incidence, and mortality data were taken from the Surveillance, Epidemiology, and End Results Program. Sibling risk, recurrence rates, and treatment costs were taken from the literature. Outcomes included life expectancy, quality-adjusted life expectancy, and lifetime costs. Cost-effectiveness ratios were in dollars per quality-adjusted life year (US dollars/QALY) gained. In the general population, screening 1 time, every 2 years, and annually saved 1.6, 4.4, and 5.2 QALYs per 1000 persons screened, with incremental cost-effectiveness ratios of US dollars 10,100/QALY, US dollars 80,700/QALY, and US dollars 586,800/QALY, respectively. In siblings of patients with melanoma (relative risk, 2.24 compared with the general population), 1-time, every-2-years, and annual screenings saved 3.6, 9.8, and 11.4 QALYs per 1000 persons screened, with incremental cost-effectiveness ratios of US dollars 4000/QALY, US dollars 35,500/QALY, and US dollars 257,800/QALY, respectively. In higher risk siblings of patients with melanoma (relative risk, 5.56), screening was more cost-effective. Results were most sensitive to screening cost, melanoma progression rate, and specificity of visual screening. One-time melanoma screening of the general population older than 50 years is very cost-effective compared with other cancer screening programs in the United States. Screening every 2 years in siblings of patients with melanoma is also cost-effective.

Projected national impact of colorectal cancer screening on clinical and economic outcomes and health services demand.

PubMed

Ladabaum, Uri; Song, Kenneth

2005-10-01

Colorectal cancer (CRC) screening is effective and cost-effective, but the potential national impact of widespread screening is uncertain. It is controversial whether screening colonoscopy can be offered widely and how emerging tests may impact health services demand. Our aim was to produce integrated, comprehensive estimates of the impact of widespread screening on national clinical and economic outcomes and health services demand. We used a Markov model and census data to estimate the national consequences of screening 75% of the US population with conventional and emerging strategies. Screening decreased CRC incidence by 17%-54% to as few as 66,000 cases per year and CRC mortality by 28%-60% to as few as 23,000 deaths per year. With no screening, total annual national CRC-related expenditures were 8.4 US billion dollars. With screening, expenditures for CRC care decreased by 1.5-4.4 US billion dollars but total expenditures increased to 9.2-15.4 US billion dollars. Screening colonoscopy every 10 years required 8.1 million colonoscopies per year including surveillance, with other strategies requiring 17%-58% as many colonoscopies. With improved screening uptake, total colonoscopy demand increased in general, even assuming substantial use of virtual colonoscopy. Despite savings in CRC care, widespread screening is unlikely to be cost saving and may increase national expenditures by 0.8-2.8 US billion dollars per year with conventional tests. The current national endoscopic capacity, as recently estimated, may be adequate to support widespread use of screening colonoscopy in the steady state. The impact of emerging tests on colonoscopy demand will depend on the extent to which they replace screening colonoscopy or increase screening uptake in the population.

Molecular-Simulation-Driven Fragment Screening for the Discovery of New CXCL12 Inhibitors.

PubMed

Martinez-Rosell, Gerard; Harvey, Matt J; De Fabritiis, Gianni

2018-03-26

Fragment-based drug discovery (FBDD) has become a mainstream approach in drug design because it allows the reduction of the chemical space and screening libraries while identifying fragments with high protein-ligand efficiency interactions that can later be grown into drug-like leads. In this work, we leverage high-throughput molecular dynamics (MD) simulations to screen a library of 129 fragments for a total of 5.85 ms against the CXCL12 monomer, a chemokine involved in inflammation and diseases such as cancer. Our in silico binding assay was able to recover binding poses, affinities, and kinetics for the selected library and was able to predict 8 mM-affinity fragments with ligand efficiencies higher than 0.3. All of the fragment hits present a similar chemical structure, with a hydrophobic core and a positively charged group, and bind to either sY7 or H1S68 pockets, where they share pharmacophoric properties with experimentally resolved natural binders. This work presents a large-scale screening assay using an exclusive combination of thousands of short MD adaptive simulations analyzed with a Markov state model (MSM) framework.

A comparison between MS-VECM and MS-VECMX on economic time series data

NASA Astrophysics Data System (ADS)

Phoong, Seuk-Wai; Ismail, Mohd Tahir; Sek, Siok-Kun

2014-07-01

Multivariate Markov switching models able to provide useful information on the study of structural change data since the regime switching model can analyze the time varying data and capture the mean and variance in the series of dependence structure. This paper will investigates the oil price and gold price effects on Malaysia, Singapore, Thailand and Indonesia stock market returns. Two forms of Multivariate Markov switching models are used namely the mean adjusted heteroskedasticity Markov Switching Vector Error Correction Model (MSMH-VECM) and the mean adjusted heteroskedasticity Markov Switching Vector Error Correction Model with exogenous variable (MSMH-VECMX). The reason for using these two models are to capture the transition probabilities of the data since real financial time series data always exhibit nonlinear properties such as regime switching, cointegrating relations, jumps or breaks passing the time. A comparison between these two models indicates that MSMH-VECM model able to fit the time series data better than the MSMH-VECMX model. In addition, it was found that oil price and gold price affected the stock market changes in the four selected countries.

Multiscale hidden Markov models for photon-limited imaging

NASA Astrophysics Data System (ADS)

Nowak, Robert D.

1999-06-01

Photon-limited image analysis is often hindered by low signal-to-noise ratios. A novel Bayesian multiscale modeling and analysis method is developed in this paper to assist in these challenging situations. In addition to providing a very natural and useful framework for modeling an d processing images, Bayesian multiscale analysis is often much less computationally demanding compared to classical Markov random field models. This paper focuses on a probabilistic graph model called the multiscale hidden Markov model (MHMM), which captures the key inter-scale dependencies present in natural image intensities. The MHMM framework presented here is specifically designed for photon-limited imagin applications involving Poisson statistics, and applications to image intensity analysis are examined.

Economic evaluation of DNA ploidy analysis vs liquid-based cytology for cervical screening.

PubMed

Nghiem, V T; Davies, K R; Beck, J R; Follen, M; MacAulay, C; Guillaud, M; Cantor, S B

2015-06-09

DNA ploidy analysis involves automated quantification of chromosomal aneuploidy, a potential marker of progression toward cervical carcinoma. We evaluated the cost-effectiveness of this method for cervical screening, comparing five ploidy strategies (using different numbers of aneuploid cells as cut points) with liquid-based Papanicolaou smear and no screening. A state-transition Markov model simulated the natural history of HPV infection and possible progression into cervical neoplasia in a cohort of 12-year-old females. The analysis evaluated cost in 2012 US$ and effectiveness in quality-adjusted life-years (QALYs) from a health-system perspective throughout a lifetime horizon in the US setting. We calculated incremental cost-effectiveness ratios (ICERs) to determine the best strategy. The robustness of optimal choices was examined in deterministic and probabilistic sensitivity analyses. In the base-case analysis, the ploidy 4 cell strategy was cost-effective, yielding an increase of 0.032 QALY and an ICER of $18 264/QALY compared to no screening. For most scenarios in the deterministic sensitivity analysis, the ploidy 4 cell strategy was the only cost-effective strategy. Cost-effectiveness acceptability curves showed that this strategy was more likely to be cost-effective than the Papanicolaou smear. Compared to the liquid-based Papanicolaou smear, screening with a DNA ploidy strategy appeared less costly and comparably effective.

Markovian prediction of future values for food grains in the economic survey

NASA Astrophysics Data System (ADS)

Sathish, S.; Khadar Babu, S. K.

2017-11-01

Now-a-days prediction and forecasting are plays a vital role in research. For prediction, regression is useful to predict the future value and current value on production process. In this paper, we assume food grain production exhibit Markov chain dependency and time homogeneity. The economic generative performance evaluation the balance time artificial fertilization different level in Estrusdetection using a daily Markov chain model. Finally, Markov process prediction gives better performance compare with Regression model.

Building Higher-Order Markov Chain Models with EXCEL

ERIC Educational Resources Information Center

Ching, Wai-Ki; Fung, Eric S.; Ng, Michael K.

2004-01-01

Categorical data sequences occur in many applications such as forecasting, data mining and bioinformatics. In this note, we present higher-order Markov chain models for modelling categorical data sequences with an efficient algorithm for solving the model parameters. The algorithm can be implemented easily in a Microsoft EXCEL worksheet. We give a…

Newborn screening by tandem mass spectrometry for glutaric aciduria type 1: a cost-effectiveness analysis.

PubMed

Pfeil, Johannes; Listl, Stefan; Hoffmann, Georg F; Kölker, Stefan; Lindner, Martin; Burgard, Peter

2013-10-17

Glutaric aciduria type I (GA-I) is a rare metabolic disorder caused by inherited deficiency of glutaryl-CoA dehydrogenase. Despite high prognostic relevance of early diagnosis and start of metabolic treatment as well as an additional cost saving potential later in life, only a limited number of countries recommend newborn screening for GA-I. So far only limited data is available enabling health care decision makers to evaluate whether investing into GA-I screening represents value for money. The aim of our study was therefore to assess the cost-effectiveness of newborn screening for GA-I by tandem mass spectrometry (MS/MS) compared to a scenario where GA-I is not included in the MS/MS screening panel. We assessed the cost-effectiveness of newborn screening for GA-I against the alternative of not including GA-I in MS/MS screening. A Markov model was developed simulating the clinical course of screened and unscreened newborns within different time horizons of 20 and 70 years. Monte Carlo simulation based probabilistic sensitivity analysis was used to determine the probability of GA-I screening representing a cost-effective therapeutic strategy. Within a 20 year time horizon, GA-I screening averts approximately 3.7 DALYs (95% CI 2.9 - 4.5) and about one life year is gained (95% CI 0.7 - 1.4) per 100,000 neonates screened initially . Moreover, the screening programme saves a total of around 30,682 Euro (95% CI 14,343 to 49,176 Euro) per 100,000 screened neonates over a 20 year time horizon. Within the limitations of the present study, extending pre-existing MS/MS newborn screening programmes by GA-I represents a highly cost-effective diagnostic strategy when assessed under conditions comparable to the German health care system.

Policy Transfer via Markov Logic Networks

NASA Astrophysics Data System (ADS)

Torrey, Lisa; Shavlik, Jude

We propose using a statistical-relational model, the Markov Logic Network, for knowledge transfer in reinforcement learning. Our goal is to extract relational knowledge from a source task and use it to speed up learning in a related target task. We show that Markov Logic Networks are effective models for capturing both source-task Q-functions and source-task policies. We apply them via demonstration, which involves using them for decision making in an initial stage of the target task before continuing to learn. Through experiments in the RoboCup simulated-soccer domain, we show that transfer via Markov Logic Networks can significantly improve early performance in complex tasks, and that transferring policies is more effective than transferring Q-functions.

Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics.

PubMed

Wu, Xiao-Lin; Sun, Chuanyu; Beissinger, Timothy M; Rosa, Guilherme Jm; Weigel, Kent A; Gatti, Natalia de Leon; Gianola, Daniel

2012-09-25

Most Bayesian models for the analysis of complex traits are not analytically tractable and inferences are based on computationally intensive techniques. This is true of Bayesian models for genome-enabled selection, which uses whole-genome molecular data to predict the genetic merit of candidate animals for breeding purposes. In this regard, parallel computing can overcome the bottlenecks that can arise from series computing. Hence, a major goal of the present study is to bridge the gap to high-performance Bayesian computation in the context of animal breeding and genetics. Parallel Monte Carlo Markov chain algorithms and strategies are described in the context of animal breeding and genetics. Parallel Monte Carlo algorithms are introduced as a starting point including their applications to computing single-parameter and certain multiple-parameter models. Then, two basic approaches for parallel Markov chain Monte Carlo are described: one aims at parallelization within a single chain; the other is based on running multiple chains, yet some variants are discussed as well. Features and strategies of the parallel Markov chain Monte Carlo are illustrated using real data, including a large beef cattle dataset with 50K SNP genotypes. Parallel Markov chain Monte Carlo algorithms are useful for computing complex Bayesian models, which does not only lead to a dramatic speedup in computing but can also be used to optimize model parameters in complex Bayesian models. Hence, we anticipate that use of parallel Markov chain Monte Carlo will have a profound impact on revolutionizing the computational tools for genomic selection programs.

Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics

PubMed Central

2012-01-01

Background Most Bayesian models for the analysis of complex traits are not analytically tractable and inferences are based on computationally intensive techniques. This is true of Bayesian models for genome-enabled selection, which uses whole-genome molecular data to predict the genetic merit of candidate animals for breeding purposes. In this regard, parallel computing can overcome the bottlenecks that can arise from series computing. Hence, a major goal of the present study is to bridge the gap to high-performance Bayesian computation in the context of animal breeding and genetics. Results Parallel Monte Carlo Markov chain algorithms and strategies are described in the context of animal breeding and genetics. Parallel Monte Carlo algorithms are introduced as a starting point including their applications to computing single-parameter and certain multiple-parameter models. Then, two basic approaches for parallel Markov chain Monte Carlo are described: one aims at parallelization within a single chain; the other is based on running multiple chains, yet some variants are discussed as well. Features and strategies of the parallel Markov chain Monte Carlo are illustrated using real data, including a large beef cattle dataset with 50K SNP genotypes. Conclusions Parallel Markov chain Monte Carlo algorithms are useful for computing complex Bayesian models, which does not only lead to a dramatic speedup in computing but can also be used to optimize model parameters in complex Bayesian models. Hence, we anticipate that use of parallel Markov chain Monte Carlo will have a profound impact on revolutionizing the computational tools for genomic selection programs. PMID:23009363

Cost-effectiveness analysis of ultrasonography screening for nonalcoholic fatty liver disease in metabolic syndrome patients

PubMed Central

Phisalprapa, Pochamana; Supakankunti, Siripen; Charatcharoenwitthaya, Phunchai; Apisarnthanarak, Piyaporn; Charoensak, Aphinya; Washirasaksiri, Chaiwat; Srivanichakorn, Weerachai; Chaiyakunapruk, Nathorn

2017-01-01

Abstract Background: Nonalcoholic fatty liver disease (NAFLD) can be diagnosed early by noninvasive ultrasonography; however, the cost-effectiveness of ultrasonography screening with intensive weight reduction program in metabolic syndrome patients is not clear. This study aims to estimate economic and clinical outcomes of ultrasonography in Thailand. Methods: Cost-effectiveness analysis used decision tree and Markov models to estimate lifetime costs and health benefits from societal perspective, based on a cohort of 509 metabolic syndrome patients in Thailand. Data were obtained from published literatures and Thai database. Results were reported as incremental cost-effectiveness ratios (ICERs) in 2014 US dollars (USD) per quality-adjusted life year (QALY) gained with discount rate of 3%. Sensitivity analyses were performed to assess the influence of parameter uncertainty on the results. Results: The ICER of ultrasonography screening of 50-year-old metabolic syndrome patients with intensive weight reduction program was 958 USD/QALY gained when compared with no screening. The probability of being cost-effective was 67% using willingness-to-pay threshold in Thailand (4848 USD/QALY gained). Screening before 45 years was cost saving while screening at 45 to 64 years was cost-effective. Conclusions: For patients with metabolic syndromes, ultrasonography screening for NAFLD with intensive weight reduction program is a cost-effective program in Thailand. Study can be used as part of evidence-informed decision making. Translational Impacts: Findings could contribute to changes of NAFLD diagnosis practice in settings where economic evidence is used as part of decision-making process. Furthermore, study design, model structure, and input parameters could also be used for future research addressing similar questions. PMID:28445256

Stochastic modelling of a single ion channel: an alternating renewal approach with application to limited time resolution.

PubMed

Milne, R K; Yeo, G F; Edeson, R O; Madsen, B W

1988-04-22

Stochastic models of ion channels have been based largely on Markov theory where individual states and transition rates must be specified, and sojourn-time densities for each state are constrained to be exponential. This study presents an approach based on random-sum methods and alternating-renewal theory, allowing individual states to be grouped into classes provided the successive sojourn times in a given class are independent and identically distributed. Under these conditions Markov models form a special case. The utility of the approach is illustrated by considering the effects of limited time resolution (modelled by using a discrete detection limit, xi) on the properties of observable events, with emphasis on the observed open-time (xi-open-time). The cumulants and Laplace transform for a xi-open-time are derived for a range of Markov and non-Markov models; several useful approximations to the xi-open-time density function are presented. Numerical studies show that the effects of limited time resolution can be extreme, and also highlight the relative importance of the various model parameters. The theory could form a basis for future inferential studies in which parameter estimation takes account of limited time resolution in single channel records. Appendixes include relevant results concerning random sums and a discussion of the role of exponential distributions in Markov models.

Low-Dose Chest Computed Tomography for Lung Cancer Screening Among Hodgkin Lymphoma Survivors: A Cost-Effectiveness Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wattson, Daniel A., E-mail: dwattson@partners.org; Hunink, M.G. Myriam; DiPiro, Pamela J.

2014-10-01

Purpose: Hodgkin lymphoma (HL) survivors face an increased risk of treatment-related lung cancer. Screening with low-dose computed tomography (LDCT) may allow detection of early stage, resectable cancers. We developed a Markov decision-analytic and cost-effectiveness model to estimate the merits of annual LDCT screening among HL survivors. Methods and Materials: Population databases and HL-specific literature informed key model parameters, including lung cancer rates and stage distribution, cause-specific survival estimates, and utilities. Relative risks accounted for radiation therapy (RT) technique, smoking status (>10 pack-years or current smokers vs not), age at HL diagnosis, time from HL treatment, and excess radiation from LDCTs.more » LDCT assumptions, including expected stage-shift, false-positive rates, and likely additional workup were derived from the National Lung Screening Trial and preliminary results from an internal phase 2 protocol that performed annual LDCTs in 53 HL survivors. We assumed a 3% discount rate and a willingness-to-pay (WTP) threshold of $50,000 per quality-adjusted life year (QALY). Results: Annual LDCT screening was cost effective for all smokers. A male smoker treated with mantle RT at age 25 achieved maximum QALYs by initiating screening 12 years post-HL, with a life expectancy benefit of 2.1 months and an incremental cost of $34,841/QALY. Among nonsmokers, annual screening produced a QALY benefit in some cases, but the incremental cost was not below the WTP threshold for any patient subsets. As age at HL diagnosis increased, earlier initiation of screening improved outcomes. Sensitivity analyses revealed that the model was most sensitive to the lung cancer incidence and mortality rates and expected stage-shift from screening. Conclusions: HL survivors are an important high-risk population that may benefit from screening, especially those treated in the past with large radiation fields including mantle or involved-field RT. Screening may be cost effective for all smokers but possibly not for nonsmokers despite a small life expectancy benefit.« less

Development and validation of a Markov microsimulation model for the economic evaluation of treatments in osteoporosis.

PubMed

Hiligsmann, Mickaël; Ethgen, Olivier; Bruyère, Olivier; Richy, Florent; Gathon, Henry-Jean; Reginster, Jean-Yves

2009-01-01

Markov models are increasingly used in economic evaluations of treatments for osteoporosis. Most of the existing evaluations are cohort-based Markov models missing comprehensive memory management and versatility. In this article, we describe and validate an original Markov microsimulation model to accurately assess the cost-effectiveness of prevention and treatment of osteoporosis. We developed a Markov microsimulation model with a lifetime horizon and a direct health-care cost perspective. The patient history was recorded and was used in calculations of transition probabilities, utilities, and costs. To test the internal consistency of the model, we carried out an example calculation for alendronate therapy. Then, external consistency was investigated by comparing absolute lifetime risk of fracture estimates with epidemiologic data. For women at age 70 years, with a twofold increase in the fracture risk of the average population, the costs per quality-adjusted life-year gained for alendronate therapy versus no treatment were estimated at €9105 and €15,325, respectively, under full and realistic adherence assumptions. All the sensitivity analyses in terms of model parameters and modeling assumptions were coherent with expected conclusions and absolute lifetime risk of fracture estimates were within the range of previous estimates, which confirmed both internal and external consistency of the model. Microsimulation models present some major advantages over cohort-based models, increasing the reliability of the results and being largely compatible with the existing state of the art, evidence-based literature. The developed model appears to be a valid model for use in economic evaluations in osteoporosis.

Hidden Markov models and other machine learning approaches in computational molecular biology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baldi, P.

1995-12-31

This tutorial was one of eight tutorials selected to be presented at the Third International Conference on Intelligent Systems for Molecular Biology which was held in the United Kingdom from July 16 to 19, 1995. Computational tools are increasingly needed to process the massive amounts of data, to organize and classify sequences, to detect weak similarities, to separate coding from non-coding regions, and reconstruct the underlying evolutionary history. The fundamental problem in machine learning is the same as in scientific reasoning in general, as well as statistical modeling: to come up with a good model for the data. In thismore » tutorial four classes of models are reviewed. They are: Hidden Markov models; artificial Neural Networks; Belief Networks; and Stochastic Grammars. When dealing with DNA and protein primary sequences, Hidden Markov models are one of the most flexible and powerful alignments and data base searches. In this tutorial, attention is focused on the theory of Hidden Markov Models, and how to apply them to problems in molecular biology.« less

Cost-effectiveness of Chagas disease screening in Latin American migrants at primary health-care centres in Europe: a Markov model analysis.

PubMed

Requena-Méndez, Ana; Bussion, Sheila; Aldasoro, Edelweiss; Jackson, Yves; Angheben, Andrea; Moore, David; Pinazo, Maria-Jesús; Gascón, Joaquim; Muñoz, Jose; Sicuri, Elisa

2017-04-01

Chagas disease is currently prevalent in European countries hosting large communities from Latin America. Whether asymptomatic individuals at risk of Chagas disease living in Europe should be screened and treated accordingly is unclear. We performed an economic evaluation of systematic Chagas disease screening of the Latin American population attending primary care centres in Europe. We constructed a decision tree model that compared the test option (screening of asymptomatic individuals, treatment, and follow-up of positive cases) with the no-test option (screening, treating, and follow-up of symptomatic individuals). The decision tree included a Markov model with five states, related to the chronic stage of the disease: indeterminate, cardiomyopathy, gastrointestinal, response to treatment, and death. The model started with a target population of 100 000 individuals, of which 4·2% (95% CI 2·2-6·8) were estimated to be infected by Trypanosoma cruzi. The primary outcome was the incremental cost-effectiveness ratio (ICER) between test and no-test options. Deterministic and probabilistic analyses (Monte Carlo simulations) were performed. In the deterministic analysis, total costs referred to 100 000 individuals in the test and no-test option were €30 903 406 and €6 597 403 respectively, with a difference of €24 306 003. The respective number of quality-adjusted life-years (QALYs) gained in the test and no-test option were 61 820·82 and 57 354·42. The ICER was €5442. In the probabilistic analysis, total costs for the test and no-test option were €32 163 649 (95% CI 31 263 705-33 063 593) and €6 904 764 (6 703 258-7 106 270), respectively. The respective number of QALYs gained was 64 634·35 (95% CI 62 809·6-66 459·1) and 59 875·73 (58 191·18-61 560·28). The difference in QALYs gained between the test and no test options was 4758·62 (95% CI 4618·42-4898·82). The incremental cost-effectiveness ratio (ICER) was €6840·75 (95% CI 2545-2759) per QALY gained for a treatment efficacy of 20% and €4243 per QALY gained for treatment efficacy of 50%. Even with a reduction in Chagas disease prevalence to 0·05% and with large variations in all the parameters, the test option would still be more cost-effective than the no-test option (less than €30000 per QALY). Screening for Chagas disease in asymptomatic Latin American adults living in Europe is a cost-effective strategy. Findings of our model provide an important element to support the implementation of T cruzi screening programmes at primary health centres in European countries hosting Latin American migrants. European Commission 7th Framework Program. Copyright © 2017 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY license. Published by Elsevier Ltd.. All rights reserved.

Markov Chain Model with Catastrophe to Determine Mean Time to Default of Credit Risky Assets

NASA Astrophysics Data System (ADS)

Dharmaraja, Selvamuthu; Pasricha, Puneet; Tardelli, Paola

2017-11-01

This article deals with the problem of probabilistic prediction of the time distance to default for a firm. To model the credit risk, the dynamics of an asset is described as a function of a homogeneous discrete time Markov chain subject to a catastrophe, the default. The behaviour of the Markov chain is investigated and the mean time to the default is expressed in a closed form. The methodology to estimate the parameters is given. Numerical results are provided to illustrate the applicability of the proposed model on real data and their analysis is discussed.

Forecasting land-cover growth using remotely sensed data: a case study of the Igneada protection area in Turkey.

PubMed

Bozkaya, A Gonca; Balcik, Filiz Bektas; Goksel, Cigdem; Esbah, Hayriye

2015-03-01

Human activities in many parts of the world have greatly affected natural areas. Therefore, monitoring and forecasting of land-cover changes are important components for sustainable utilization, conservation, and development of these areas. This research has been conducted on Igneada, a legally protected area on the northwest coast of Turkey, which is famous for its unique, mangrove forests. The main focus of this study was to apply a land use and cover model that could quantitatively and graphically present the changes and its impacts on Igneada landscapes in the future. In this study, a Markov chain-based, stochastic Markov model and cellular automata Markov model were used. These models were calibrated using a time series of developed areas derived from Landsat Thematic Mapper (TM) imagery between 1990 and 2010 that also projected future growth to 2030. The results showed that CA Markov yielded reliable information better than St. Markov model. The findings displayed constant but overall slight increase of settlement and forest cover, and slight decrease of agricultural lands. However, even the slightest unsustainable change can put a significant pressure on the sensitive ecosystems of Igneada. Therefore, the management of the protected area should not only focus on the landscape composition but also pay attention to landscape configuration.

LECTURES ON GAME THEORY, MARKOV CHAINS, AND RELATED TOPICS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thompson, G L

1958-03-01

Notes on nine lectures delivered at Sandin Corporation in August 1957 are given. Part one contains the manuscript of a paper concerning a judging problem. Part two is concerned with finite Markov-chain theory amd discusses regular Markov chains, absorbing Markov chains, the classification of states, application to the Leontief input-output model, and semimartingales. Part three contains notes on game theory and covers matrix games, the effect of psychological attitudes on the outcomes of games, extensive games, amd matrix theory applied to mathematical economics. (auth)

3D geometric modeling and simulation of laser propagation through turbulence with plenoptic functions

NASA Astrophysics Data System (ADS)

Wu, Chensheng; Nelson, William; Davis, Christopher C.

2014-10-01

Plenoptic functions are functions that preserve all the necessary light field information of optical events. Theoretical work has demonstrated that geometric based plenoptic functions can serve equally well in the traditional wave propagation equation known as the "scalar stochastic Helmholtz equation". However, in addressing problems of 3D turbulence simulation, the dominant methods using phase screen models have limitations both in explaining the choice of parameters (on the transverse plane) in real-world measurements, and finding proper correlations between neighboring phase screens (the Markov assumption breaks down). Though possible corrections to phase screen models are still promising, the equivalent geometric approach based on plenoptic functions begins to show some advantages. In fact, in these geometric approaches, a continuous wave problem is reduced to discrete trajectories of rays. This allows for convenience in parallel computing and guarantees conservation of energy. Besides the pairwise independence of simulated rays, the assigned refractive index grids can be directly tested by temperature measurements with tiny thermoprobes combined with other parameters such as humidity level and wind speed. Furthermore, without loss of generality one can break the causal chain in phase screen models by defining regional refractive centers to allow rays that are less affected to propagate through directly. As a result, our work shows that the 3D geometric approach serves as an efficient and accurate method in assessing relevant turbulence problems with inputs of several environmental measurements and reasonable guesses (such as Cn 2 levels). This approach will facilitate analysis and possible corrections in lateral wave propagation problems, such as image de-blurring, prediction of laser propagation over long ranges, and improvement of free space optic communication systems. In this paper, the plenoptic function model and relevant parallel algorithm computing will be presented, and its primary results and applications are demonstrated.

Discrete time Markov chains (DTMC) susceptible infected susceptible (SIS) epidemic model with two pathogens in two patches

NASA Astrophysics Data System (ADS)

Lismawati, Eka; Respatiwulan; Widyaningsih, Purnami

2017-06-01

The SIS epidemic model describes the pattern of disease spread with characteristics that recovered individuals can be infected more than once. The number of susceptible and infected individuals every time follows the discrete time Markov process. It can be represented by the discrete time Markov chains (DTMC) SIS. The DTMC SIS epidemic model can be developed for two pathogens in two patches. The aims of this paper are to reconstruct and to apply the DTMC SIS epidemic model with two pathogens in two patches. The model was presented as transition probabilities. The application of the model obtain that the number of susceptible individuals decreases while the number of infected individuals increases for each pathogen in each patch.

The management of aldosterone-producing adrenal adenomas--does adrenalectomy increase costs?

PubMed

Reimel, Bethann; Zanocco, Kyle; Russo, Mark J; Zarnegar, Rasa; Clark, Orlo H; Allendorf, John D; Chabot, John A; Duh, Quan-Yang; Lee, James A; Sturgeon, Cord

2010-12-01

Most experts agree that primary hyperaldosteronism (PHA) caused by an aldosterone-producing adenoma (APA) is best treated by adrenalectomy. From a public health standpoint, the cost of treatment must be considered. We sought to compare the current guideline-based (surgical) strategy with universal pharmacologic management to determine the optimal strategy from a cost perspective. A decision analysis was performed using a Markov state transition model comparing the strategies for PHA treatment. Pharmacologic management for all patients with PHA was compared with a strategy of screening for and resecting an aldosterone-producing adenoma. Success rates were determined for treatment outcomes based on a literature review. Medicare reimbursement rates were calculated to estimate costs from a third-party payer perspective. Screening for and resecting APAs was the least costly strategy in this model. For a reference patient with 41 remaining years of life, the discounted expected cost of the surgical strategy was $27,821. The discounted expected cost of the medical strategy was $34,691. The cost of adrenalectomy would have to increase by 156% to $22,525 from $8,784 for universal pharmacologic therapy to be less costly. Screening for APA is more costly if fewer than 9.6% of PHA patients have resectable APA. Resection of APAs was the least costly treatment strategy in this decision analysis model. Copyright © 2010 Mosby, Inc. All rights reserved.

A cost-benefit analysis of a proposed overseas refugee latent tuberculosis infection screening and treatment program.

PubMed

Wingate, La'Marcus T; Coleman, Margaret S; de la Motte Hurst, Christopher; Semple, Marie; Zhou, Weigong; Cetron, Martin S; Painter, John A

2015-12-01

This study explored the effect of screening and treatment of refugees for latent tuberculosis infection (LTBI) before entrance to the United States as a strategy for reducing active tuberculosis (TB). The purpose of this study was to estimate the costs and benefits of LTBI screening and treatment in United States bound refugees prior to arrival. Costs were included for foreign and domestic LTBI screening and treatment and the domestic treatment of active TB. A decision tree with multiple Markov nodes was developed to determine the total costs and number of active TB cases that occurred in refugee populations that tested 55, 35, and 20 % tuberculin skin test positive under two models: no overseas LTBI screening and overseas LTBI screening and treatment. For this analysis, refugees that tested 55, 35, and 20 % tuberculin skin test positive were divided into high, moderate, and low LTBI prevalence categories to denote their prevalence of LTBI relative to other refugee populations. For a hypothetical 1-year cohort of 100,000 refugees arriving in the United States from regions with high, moderate, and low LTBI prevalence, implementation of overseas screening would be expected to prevent 440, 220, and 57 active TB cases in the United States during the first 20 years after arrival. The cost savings associated with treatment of these averted cases would offset the cost of LTBI screening and treatment for refugees from countries with high (net cost-saving: $4.9 million) and moderate (net cost-saving: $1.6 million) LTBI prevalence. For low LTBI prevalence populations, LTBI screening and treatment exceed expected future TB treatment cost savings (net cost of $780,000). Implementing LTBI screening and treatment for United States bound refugees from countries with high or moderate LTBI prevalence would potentially save millions of dollars and contribute to United States TB elimination goals. These estimates are conservative since secondary transmission from tuberculosis cases in the United States was not considered in the model.

Colorectal Cancer Screening: How Health Gains and Cost-Effectiveness Vary by Ethnic Group, the Impact on Health Inequalities, and the Optimal Age Range to Screen.

PubMed

McLeod, Melissa; Kvizhinadze, Giorgi; Boyd, Matt; Barendregt, Jan; Sarfati, Diana; Wilson, Nick; Blakely, Tony

2017-09-01

Background: Screening programs consistently underserve indigenous populations despite a higher overall burden of cancer. In this study, we explore the likely health gains and cost-effectiveness of a national colorectal cancer screening program for the indigenous Māori population of New Zealand (NZ). Methods: A Markov model estimated: health benefits (quality-adjusted life-year; QALY), costs, and cost-effectiveness of biennial immunochemical fecal occult blood testing (FOBTi) of 50- to 74-year-olds from 2011. Input parameters came from literature reviews, the NZ Bowel Screening Programme Pilot, and NZ linked health datasets. Equity analyses substituted non-Māori values for Māori values of background (noncolorectal cancer) morbidity and mortality, colorectal cancer survival and incidence, screening coverage, and stage-specific survival. We measured the change in "quality-adjusted life expectancy" (QALE) as a result of the intervention. Results: Based upon a threshold of GDP per capita (NZ$45,000), colorectal cancer screening in NZ using FOBTi is cost-effective: NZ$2,930 (US$1,970) per QALY gained [95% uncertainty interval: cost saving to $6,850 (US$4,610)]. Modeled health gains per capita for Māori were less than for non-Māori: half for 50- to 54-year-olds (0.031 QALYs per person for Māori vs. 0.058 for non-Māori), and a fifth (0.003 c.f. 0.016) for 70- to 74-year-olds and ethnic inequalities in QALE increased with colorectal cancer screening. Conclusions: Colorectal cancer screening in NZ using FOBTi is likely to be cost-effective but risks increasing inequalities in health for Māori. Impact: To avoid or mitigate the generation of further health inequalities, attention should be given to underserved population groups when planning and implementing screening programs. Cancer Epidemiol Biomarkers Prev; 26(9); 1391-400. ©2017 AACR . ©2017 American Association for Cancer Research.

Identifying and correcting non-Markov states in peptide conformational dynamics

NASA Astrophysics Data System (ADS)

Nerukh, Dmitry; Jensen, Christian H.; Glen, Robert C.

2010-02-01

Conformational transitions in proteins define their biological activity and can be investigated in detail using the Markov state model. The fundamental assumption on the transitions between the states, their Markov property, is critical in this framework. We test this assumption by analyzing the transitions obtained directly from the dynamics of a molecular dynamics simulated peptide valine-proline-alanine-leucine and states defined phenomenologically using clustering in dihedral space. We find that the transitions are Markovian at the time scale of ≈50 ps and longer. However, at the time scale of 30-40 ps the dynamics loses its Markov property. Our methodology reveals the mechanism that leads to non-Markov behavior. It also provides a way of regrouping the conformations into new states that now possess the required Markov property of their dynamics.

Detecting memory and structure in human navigation patterns using Markov chain models of varying order.

PubMed

Singer, Philipp; Helic, Denis; Taraghi, Behnam; Strohmaier, Markus

2014-01-01

One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Google's PageRank algorithm and others. Recently, new studies suggested that human navigation may better be modeled using higher order Markov chain models, i.e., the next page depends on a longer history of past clicks. Yet, this finding is preliminary and does not account for the higher complexity of higher order Markov chain models which is why the memoryless model is still widely used. In this work we thoroughly present a diverse array of advanced inference methods for determining the appropriate Markov chain order. We highlight strengths and weaknesses of each method and apply them for investigating memory and structure of human navigation on the Web. Our experiments reveal that the complexity of higher order models grows faster than their utility, and thus we confirm that the memoryless model represents a quite practical model for human navigation on a page level. However, when we expand our analysis to a topical level, where we abstract away from specific page transitions to transitions between topics, we find that the memoryless assumption is violated and specific regularities can be observed. We report results from experiments with two types of navigational datasets (goal-oriented vs. free form) and observe interesting structural differences that make a strong argument for more contextual studies of human navigation in future work.

Detecting Memory and Structure in Human Navigation Patterns Using Markov Chain Models of Varying Order

PubMed Central

Singer, Philipp; Helic, Denis; Taraghi, Behnam; Strohmaier, Markus

2014-01-01

One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Google's PageRank algorithm and others. Recently, new studies suggested that human navigation may better be modeled using higher order Markov chain models, i.e., the next page depends on a longer history of past clicks. Yet, this finding is preliminary and does not account for the higher complexity of higher order Markov chain models which is why the memoryless model is still widely used. In this work we thoroughly present a diverse array of advanced inference methods for determining the appropriate Markov chain order. We highlight strengths and weaknesses of each method and apply them for investigating memory and structure of human navigation on the Web. Our experiments reveal that the complexity of higher order models grows faster than their utility, and thus we confirm that the memoryless model represents a quite practical model for human navigation on a page level. However, when we expand our analysis to a topical level, where we abstract away from specific page transitions to transitions between topics, we find that the memoryless assumption is violated and specific regularities can be observed. We report results from experiments with two types of navigational datasets (goal-oriented vs. free form) and observe interesting structural differences that make a strong argument for more contextual studies of human navigation in future work. PMID:25013937

[Cost-effectiveness of breast cancer screening policies in Mexico].

PubMed

Valencia-Mendoza, Atanacio; Sánchez-González, Gilberto; Bautista-Arredondo, Sergio; Torres-Mejía, Gabriela; Bertozzi, Stefano M

2009-01-01

Generate cost-effectiveness information to allow policy makers optimize breast cancer (BC) policy in Mexico. We constructed a Markov model that incorporates four interrelated processes of the disease: the natural history; detection using mammography; treatment; and other competing-causes mortality, according to which 13 different strategies were modeled. Strategies (starting age, % of coverage, frequency in years)= (48, 25, 2), (40, 50, 2) and (40, 50, 1) constituted the optimal method for expanding the BC program, yielding 75.3, 116.4 and 171.1 thousand pesos per life-year saved, respectively. The strategies included in the optimal method for expanding the program produce a cost per life-year saved of less than two times the GNP per capita and hence are cost-effective according to WHO Commission on Macroeconomics and Health criteria.

Markov Modeling of Component Fault Growth over a Derived Domain of Feasible Output Control Effort Modifications

NASA Technical Reports Server (NTRS)

Bole, Brian; Goebel, Kai; Vachtsevanos, George

2012-01-01

This paper introduces a novel Markov process formulation of stochastic fault growth modeling, in order to facilitate the development and analysis of prognostics-based control adaptation. A metric representing the relative deviation between the nominal output of a system and the net output that is actually enacted by an implemented prognostics-based control routine, will be used to define the action space of the formulated Markov process. The state space of the Markov process will be defined in terms of an abstracted metric representing the relative health remaining in each of the system s components. The proposed formulation of component fault dynamics will conveniently relate feasible system output performance modifications to predictions of future component health deterioration.

Scalable approximate policies for Markov decision process models of hospital elective admissions.

PubMed

Zhu, George; Lizotte, Dan; Hoey, Jesse

2014-05-01

To demonstrate the feasibility of using stochastic simulation methods for the solution of a large-scale Markov decision process model of on-line patient admissions scheduling. The problem of admissions scheduling is modeled as a Markov decision process in which the states represent numbers of patients using each of a number of resources. We investigate current state-of-the-art real time planning methods to compute solutions to this Markov decision process. Due to the complexity of the model, traditional model-based planners are limited in scalability since they require an explicit enumeration of the model dynamics. To overcome this challenge, we apply sample-based planners along with efficient simulation techniques that given an initial start state, generate an action on-demand while avoiding portions of the model that are irrelevant to the start state. We also propose a novel variant of a popular sample-based planner that is particularly well suited to the elective admissions problem. Results show that the stochastic simulation methods allow for the problem size to be scaled by a factor of almost 10 in the action space, and exponentially in the state space. We have demonstrated our approach on a problem with 81 actions, four specialities and four treatment patterns, and shown that we can generate solutions that are near-optimal in about 100s. Sample-based planners are a viable alternative to state-based planners for large Markov decision process models of elective admissions scheduling. Copyright © 2014 Elsevier B.V. All rights reserved.

Towards automatic Markov reliability modeling of computer architectures

NASA Technical Reports Server (NTRS)

Liceaga, C. A.; Siewiorek, D. P.

1986-01-01

The analysis and evaluation of reliability measures using time-varying Markov models is required for Processor-Memory-Switch (PMS) structures that have competing processes such as standby redundancy and repair, or renewal processes such as transient or intermittent faults. The task of generating these models is tedious and prone to human error due to the large number of states and transitions involved in any reasonable system. Therefore model formulation is a major analysis bottleneck, and model verification is a major validation problem. The general unfamiliarity of computer architects with Markov modeling techniques further increases the necessity of automating the model formulation. This paper presents an overview of the Automated Reliability Modeling (ARM) program, under development at NASA Langley Research Center. ARM will accept as input a description of the PMS interconnection graph, the behavior of the PMS components, the fault-tolerant strategies, and the operational requirements. The output of ARM will be the reliability of availability Markov model formulated for direct use by evaluation programs. The advantages of such an approach are (a) utility to a large class of users, not necessarily expert in reliability analysis, and (b) a lower probability of human error in the computation.

Modelling Faculty Replacement Strategies Using a Time-Dependent Finite Markov-Chain Process.

ERIC Educational Resources Information Center

Hackett, E. Raymond; Magg, Alexander A.; Carrigan, Sarah D.

1999-01-01

Describes the use of a time-dependent Markov-chain model to develop faculty-replacement strategies within a college at a research university. The study suggests that a stochastic modelling approach can provide valuable insight when planning for personnel needs in the immediate (five-to-ten year) future. (MSE)

The introduction of hydrogen bond and hydrophobicity effects into the rotational isomeric states model for conformational analysis of unfolded peptides.

PubMed

Engin, Ozge; Sayar, Mehmet; Erman, Burak

2009-01-13

Relative contributions of local and non-local interactions to the unfolded conformations of peptides are examined by using the rotational isomeric states model which is a Markov model based on pairwise interactions of torsion angles. The isomeric states of a residue are well described by the Ramachandran map of backbone torsion angles. The statistical weight matrices for the states are determined by molecular dynamics simulations applied to monopeptides and dipeptides. Conformational properties of tripeptides formed from combinations of alanine, valine, tyrosine and tryptophan are investigated based on the Markov model. Comparison with molecular dynamics simulation results on these tripeptides identifies the sequence-distant long-range interactions that are missing in the Markov model. These are essentially the hydrogen bond and hydrophobic interactions that are obtained between the first and the third residue of a tripeptide. A systematic correction is proposed for incorporating these long-range interactions into the rotational isomeric states model. Preliminary results suggest that the Markov assumption can be improved significantly by renormalizing the statistical weight matrices to include the effects of the long-range correlations.

The introduction of hydrogen bond and hydrophobicity effects into the rotational isomeric states model for conformational analysis of unfolded peptides

NASA Astrophysics Data System (ADS)

Engin, Ozge; Sayar, Mehmet; Erman, Burak

2009-03-01

Relative contributions of local and non-local interactions to the unfolded conformations of peptides are examined by using the rotational isomeric states model which is a Markov model based on pairwise interactions of torsion angles. The isomeric states of a residue are well described by the Ramachandran map of backbone torsion angles. The statistical weight matrices for the states are determined by molecular dynamics simulations applied to monopeptides and dipeptides. Conformational properties of tripeptides formed from combinations of alanine, valine, tyrosine and tryptophan are investigated based on the Markov model. Comparison with molecular dynamics simulation results on these tripeptides identifies the sequence-distant long-range interactions that are missing in the Markov model. These are essentially the hydrogen bond and hydrophobic interactions that are obtained between the first and the third residue of a tripeptide. A systematic correction is proposed for incorporating these long-range interactions into the rotational isomeric states model. Preliminary results suggest that the Markov assumption can be improved significantly by renormalizing the statistical weight matrices to include the effects of the long-range correlations.

Bacterial genomes lacking long-range correlations may not be modeled by low-order Markov chains: the role of mixing statistics and frame shift of neighboring genes.

PubMed

Cocho, Germinal; Miramontes, Pedro; Mansilla, Ricardo; Li, Wentian

2014-12-01

We examine the relationship between exponential correlation functions and Markov models in a bacterial genome in detail. Despite the well known fact that Markov models generate sequences with correlation function that decays exponentially, simply constructed Markov models based on nearest-neighbor dimer (first-order), trimer (second-order), up to hexamer (fifth-order), and treating the DNA sequence as being homogeneous all fail to predict the value of exponential decay rate. Even reading-frame-specific Markov models (both first- and fifth-order) could not explain the fact that the exponential decay is very slow. Starting with the in-phase coding-DNA-sequence (CDS), we investigated correlation within a fixed-codon-position subsequence, and in artificially constructed sequences by packing CDSs with out-of-phase spacers, as well as altering CDS length distribution by imposing an upper limit. From these targeted analyses, we conclude that the correlation in the bacterial genomic sequence is mainly due to a mixing of heterogeneous statistics at different codon positions, and the decay of correlation is due to the possible out-of-phase between neighboring CDSs. There are also small contributions to the correlation from bases at the same codon position, as well as by non-coding sequences. These show that the seemingly simple exponential correlation functions in bacterial genome hide a complexity in correlation structure which is not suitable for a modeling by Markov chain in a homogeneous sequence. Other results include: use of the (absolute value) second largest eigenvalue to represent the 16 correlation functions and the prediction of a 10-11 base periodicity from the hexamer frequencies. Copyright © 2014 Elsevier Ltd. All rights reserved.

Analysing grouping of nucleotides in DNA sequences using lumped processes constructed from Markov chains.

PubMed

Guédon, Yann; d'Aubenton-Carafa, Yves; Thermes, Claude

2006-03-01

The most commonly used models for analysing local dependencies in DNA sequences are (high-order) Markov chains. Incorporating knowledge relative to the possible grouping of the nucleotides enables to define dedicated sub-classes of Markov chains. The problem of formulating lumpability hypotheses for a Markov chain is therefore addressed. In the classical approach to lumpability, this problem can be formulated as the determination of an appropriate state space (smaller than the original state space) such that the lumped chain defined on this state space retains the Markov property. We propose a different perspective on lumpability where the state space is fixed and the partitioning of this state space is represented by a one-to-many probabilistic function within a two-level stochastic process. Three nested classes of lumped processes can be defined in this way as sub-classes of first-order Markov chains. These lumped processes enable parsimonious reparameterizations of Markov chains that help to reveal relevant partitions of the state space. Characterizations of the lumped processes on the original transition probability matrix are derived. Different model selection methods relying either on hypothesis testing or on penalized log-likelihood criteria are presented as well as extensions to lumped processes constructed from high-order Markov chains. The relevance of the proposed approach to lumpability is illustrated by the analysis of DNA sequences. In particular, the use of lumped processes enables to highlight differences between intronic sequences and gene untranslated region sequences.

Cost-Effectiveness of High-Risk Human Papillomavirus Testing With Messenger RNA Versus DNA Under United States Guidelines for Cervical Cancer Screening.

PubMed

Ting, Jie; Smith, Jennifer S; Myers, Evan R

2015-10-01

To compare the cost-effectiveness of high-risk human papillomavirus (hrHPV) testing using a hrHPV DNA and a hrHPV messenger RNA (mRNA) assay under current US cervical cancer screening guidelines. We constructed a Markov model for stochastic cost-effectiveness analysis using published data. We compared screening efficiency using DNA and mRNA testing for the following: (1) cotesting with cytology in women 30 to 65 years, and (2) triage of women with mild cervical cytological abnormalities (atypical squamous cells of undetermined significance [ASC-US]) in the United States. Screening end point is histologically confirmed high-grade lesions (cervical intraepithelial neoplasia grade 2, 3, or invasive cancer). Sensitivity and specificity estimates of DNA and mRNA testing to detect cervical intraepithelial neoplasia grade 2, 3, or invasive cancer were obtained from 2 published trials: the US Clinical Evaluation of APTIMA mRNA (CLEAR) study for ASC-US triage and the French APTIMA Screening Evaluation (FASE) study for cotesting. Costs of DNA and mRNA testing were assumed identical. Costs of screening, diagnosis, and treatment of cervical neoplasia and cancer were from previously published estimates, adjusted to 2012 US dollars. Inputs were modeled as distributions for Monte Carlo probabilistic sensitivity analysis. Model outcomes were costs per life-year saved for each strategy, discounted at 3% annually. For both cotesting and ASC-US triage, mRNA testing cost less than DNA testing, whereas life expectancies were widely overlapping. There was a 100% probability that DNA testing was not cost-effective at $100,000/life-year saved threshold for ASC-US triage and a 55% probability that DNA testing was not cost-effective at the same threshold for cotesting. Based on the available evidence, mRNA testing for cotesting or ASC-US triage is likely to be more efficient than DNA testing under current US cervical cancer screening guidelines.

Financial effect of instituting Deficit Reduction Act documentation requirements in family planning clinics in Oregon.

PubMed

Rodriguez, Maria Isabel; Angus, Lisa; Elman, Emily; Darney, Philip D; Caughey, Aaron B

2011-06-01

The study was conducted to estimate the long-term costs for implementing citizenship documentation requirements in a Medicaid expansion program for family planning services in Oregon. A decision-analytic model was developed using two perspectives: the state and society. Our primary outcome was future reproductive health care costs due to pregnancy in the next 5 years. A Markov structure was utilized to capture multiple future pregnancies. Model inputs were retrieved from the existing literature and local hospital and Medicaid data related to reimbursements. One-way and multi-way sensitivity analyses were conducted. A Monte Carlo simulation was performed to simultaneously incorporate uncertainty from all of the model inputs. Screening for citizenship results in a loss of $3119 over 5 years ($39,382 vs. $42,501) for the state and $4209 for society ($63,391 compared to $59,182) for adult women. Among adolescents, requiring proof of identity and citizenship results in a loss of $3123 for the state ($39,378 versus $42,501) and $4214 for society ($63,391 instead of $59,177). Screening for citizenship status in publicly funded family planning clinics leads to financial losses for the state and society. Copyright © 2011 Elsevier Inc. All rights reserved.

Linear system identification via backward-time observer models

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Phan, Minh

1993-01-01

This paper presents an algorithm to identify a state-space model of a linear system using a backward-time approach. The procedure consists of three basic steps. First, the Markov parameters of a backward-time observer are computed from experimental input-output data. Second, the backward-time observer Markov parameters are decomposed to obtain the backward-time system Markov parameters (backward-time pulse response samples) from which a backward-time state-space model is realized using the Eigensystem Realization Algorithm. Third, the obtained backward-time state space model is converted to the usual forward-time representation. Stochastic properties of this approach will be discussed. Experimental results are given to illustrate when and to what extent this concept works.

Technical manual for basic version of the Markov chain nest productivity model (MCnest)

EPA Science Inventory

The Markov Chain Nest Productivity Model (or MCnest) integrates existing toxicity information from three standardized avian toxicity tests with information on species life history and the timing of pesticide applications relative to the timing of avian breeding seasons to quantit...

User’s manual for basic version of MCnest Markov chain nest productivity model

EPA Science Inventory

The Markov Chain Nest Productivity Model (or MCnest) integrates existing toxicity information from three standardized avian toxicity tests with information on species life history and the timing of pesticide applications relative to the timing of avian breeding seasons to quantit...

A simplified parsimonious higher order multivariate Markov chain model with new convergence condition

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, we present a simplified parsimonious higher-order multivariate Markov chain model with new convergence condition. (TPHOMMCM-NCC). Moreover, estimation method of the parameters in TPHOMMCM-NCC is give. Numerical experiments illustrate the effectiveness of TPHOMMCM-NCC.

Linear system identification via backward-time observer models

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Phan, Minh Q.

1992-01-01

Presented here is an algorithm to compute the Markov parameters of a backward-time observer for a backward-time model from experimental input and output data. The backward-time observer Markov parameters are decomposed to obtain the backward-time system Markov parameters (backward-time pulse response samples) for the backward-time system identification. The identified backward-time system Markov parameters are used in the Eigensystem Realization Algorithm to identify a backward-time state-space model, which can be easily converted to the usual forward-time representation. If one reverses time in the model to be identified, what were damped true system modes become modes with negative damping, growing as the reversed time increases. On the other hand, the noise modes in the identification still maintain the property that they are stable. The shift from positive damping to negative damping of the true system modes allows one to distinguish these modes from noise modes. Experimental results are given to illustrate when and to what extent this concept works.

Techniques for modeling the reliability of fault-tolerant systems with the Markov state-space approach

NASA Technical Reports Server (NTRS)

Butler, Ricky W.; Johnson, Sally C.

1995-01-01

This paper presents a step-by-step tutorial of the methods and the tools that were used for the reliability analysis of fault-tolerant systems. The approach used in this paper is the Markov (or semi-Markov) state-space method. The paper is intended for design engineers with a basic understanding of computer architecture and fault tolerance, but little knowledge of reliability modeling. The representation of architectural features in mathematical models is emphasized. This paper does not present details of the mathematical solution of complex reliability models. Instead, it describes the use of several recently developed computer programs SURE, ASSIST, STEM, and PAWS that automate the generation and the solution of these models.

Housing Value Projection Model Related to Educational Planning: The Feasibility of a New Methodology. Final Report.

ERIC Educational Resources Information Center

Helbock, Richard W.; Marker, Gordon

This study concerns the feasibility of a Markov chain model for projecting housing values and racial mixes. Such projections could be used in planning the layout of school districts to achieve desired levels of socioeconomic heterogeneity. Based upon the concepts and assumptions underlying a Markov chain model, it is concluded that such a model is…

Noise can speed convergence in Markov chains.

PubMed

Franzke, Brandon; Kosko, Bart

2011-10-01

A new theorem shows that noise can speed convergence to equilibrium in discrete finite-state Markov chains. The noise applies to the state density and helps the Markov chain explore improbable regions of the state space. The theorem ensures that a stochastic-resonance noise benefit exists for states that obey a vector-norm inequality. Such noise leads to faster convergence because the noise reduces the norm components. A corollary shows that a noise benefit still occurs if the system states obey an alternate norm inequality. This leads to a noise-benefit algorithm that requires knowledge of the steady state. An alternative blind algorithm uses only past state information to achieve a weaker noise benefit. Simulations illustrate the predicted noise benefits in three well-known Markov models. The first model is a two-parameter Ehrenfest diffusion model that shows how noise benefits can occur in the class of birth-death processes. The second model is a Wright-Fisher model of genotype drift in population genetics. The third model is a chemical reaction network of zeolite crystallization. A fourth simulation shows a convergence rate increase of 64% for states that satisfy the theorem and an increase of 53% for states that satisfy the corollary. A final simulation shows that even suboptimal noise can speed convergence if the noise applies over successive time cycles. Noise benefits tend to be sharpest in Markov models that do not converge quickly and that do not have strong absorbing states.

The algebra of the general Markov model on phylogenetic trees and networks.

PubMed

Sumner, J G; Holland, B R; Jarvis, P D

2012-04-01

It is known that the Kimura 3ST model of sequence evolution on phylogenetic trees can be extended quite naturally to arbitrary split systems. However, this extension relies heavily on mathematical peculiarities of the associated Hadamard transformation, and providing an analogous augmentation of the general Markov model has thus far been elusive. In this paper, we rectify this shortcoming by showing how to extend the general Markov model on trees to include incompatible edges; and even further to more general network models. This is achieved by exploring the algebra of the generators of the continuous-time Markov chain together with the “splitting” operator that generates the branching process on phylogenetic trees. For simplicity, we proceed by discussing the two state case and then show that our results are easily extended to more states with little complication. Intriguingly, upon restriction of the two state general Markov model to the parameter space of the binary symmetric model, our extension is indistinguishable from the Hadamard approach only on trees; as soon as any incompatible splits are introduced the two approaches give rise to differing probability distributions with disparate structure. Through exploration of a simple example, we give an argument that our extension to more general networks has desirable properties that the previous approaches do not share. In particular, our construction allows for convergent evolution of previously divergent lineages; a property that is of significant interest for biological applications.

Disease burden of chronic hepatitis B among immigrants in Canada.

PubMed

Wong, William W L; Woo, Gloria; Heathcote, E Jenny; Krahn, Murray

2013-03-01

The prevalence of chronic hepatitis B (CHB) infection among immigrants to North America ranges from 2% to 15%, 40% of whom develop advanced liver disease. Screening for hepatitis B surface antigen is not recommended for immigrants. To estimate the disease burden of CHB among immigrants in Canada using Markov cohort models comparing a cohort of immigrants with CHB versus a control cohort of immigrants without CHB. Markov cohort models were used to estimate life years, quality-adjusted life years and lifetime direct medical costs (adjusted to 2008 Canadian dollars) for a cohort of immigrants with CHB living in Canada in 2006, and an age-matched control cohort of immigrants without CHB living in Canada in 2006. Parameter values were derived from the published literature. At the baseline estimate, the model suggested that the cohort of immigrants with CHB lost an average of 4.6 life years (corresponding to 1.5 quality-adjusted life years), had an increased average of $24,249 for lifetime direct medical costs, and had a higher lifetime risk for decompensated cirrhosis (12%), hepatocellular carcinoma (16%) and need for liver transplant (5%) when compared with the control cohort. Results of the present study showed that the socio-economic burden of CHB among immigrants living in Canada is substantial. Governments and health systems need to develop policies that promote early recognition of CHB and raise public awareness regarding hepatitis B to extend the lives of infected immigrants.

Disease burden of chronic hepatitis B among immigrants in Canada

PubMed Central

Wong, William WL; Woo, Gloria; Heathcote, E Jenny; Krahn, Murray

2013-01-01

BACKGROUND: The prevalence of chronic hepatitis B (CHB) infection among immigrants to North America ranges from 2% to 15%, 40% of whom develop advanced liver disease. Screening for hepatitis B surface antigen is not recommended for immigrants. OBJECTIVE: To estimate the disease burden of CHB among immigrants in Canada using Markov cohort models comparing a cohort of immigrants with CHB versus a control cohort of immigrants without CHB. METHODS: Markov cohort models were used to estimate life years, quality-adjusted life years and lifetime direct medical costs (adjusted to 2008 Canadian dollars) for a cohort of immigrants with CHB living in Canada in 2006, and an age-matched control cohort of immigrants without CHB living in Canada in 2006. Parameter values were derived from the published literature. RESULTS: At the baseline estimate, the model suggested that the cohort of immigrants with CHB lost an average of 4.6 life years (corresponding to 1.5 quality-adjusted life years), had an increased average of $24,249 for lifetime direct medical costs, and had a higher lifetime risk for decompensated cirrhosis (12%), hepatocellular carcinoma (16%) and need for liver transplant (5%) when compared with the control cohort. DISCUSSION: Results of the present study showed that the socio-economic burden of CHB among immigrants living in Canada is sub-stantial. Governments and health systems need to develop policies that promote early recognition of CHB and raise public awareness regarding hepatitis B to extend the lives of infected immigrants. PMID:23516678

Cost-effectiveness analysis of cardiovascular risk factor screening in women who experienced hypertensive pregnancy disorders at term.

PubMed

van Baaren, Gert-Jan; Hermes, Wietske; Franx, Arie; van Pampus, Maria G; Bloemenkamp, Kitty W M; van der Post, Joris A; Porath, Martina; Ponjee, Gabrielle A E; Tamsma, Jouke T; Mol, Ben Willem J; Opmeer, Brent C; de Groot, Christianne J M

2014-10-01

To assess the cost-effectiveness of post-partum screening on cardiovascular risk factors and subsequent treatment in women with a history of gestational hypertension or pre-eclampsia at term. Two separate Markov models evaluated the cost-effectiveness analysis of hypertension (HT) screening and screening on metabolic syndrome (MetS), respectively, as compared to current practice in women with a history of term hypertensive pregnancy disorders. Analyses were performed from the Dutch health care perspective, using a lifetime horizon. One-way sensitivity analyses and Monte Carlo simulation evaluated the robustness of the results. Both screening on HT and MetS in women with a history of gestational hypertension or pre-eclampsia resulted in increase in life expectancy (HT screening 0.23year (95% CI -0.06 to 0.54); MetS screening 0.14years (95% CI -0.16 to 0.45)). The gain in QALYs was limited, with HT screening and MetS screening generating 0.04 QALYs (95% CI -0.12 to 0.20) and 0.03 QALYs (95% CI -0.14 to 0.19), resulting in costs to gain one QALY of €4228 and €28,148, respectively. Analyses for uncertainty showed a chance of 74% and 75%, respectively, that post-partum screening is cost-effective at a threshold of €60,000/QALY. According to the available knowledge post-partum screening on cardiovascular risk factors and subsequent treatment in women with a history of gestational hypertension or pre-eclampsia at term is likely to be cost-effective. Copyright © 2014 International Society for the Study of Hypertension in Pregnancy. Published by Elsevier B.V. All rights reserved.

Analysis and design of a second-order digital phase-locked loop

NASA Technical Reports Server (NTRS)

Blasche, P. R.

1979-01-01

A specific second-order digital phase-locked loop (DPLL) was modeled as a first-order Markov chain with alternatives. From the matrix of transition probabilities of the Markov chain, the steady-state phase error of the DPLL was determined. In a similar manner the loop's response was calculated for a fading input. Additionally, a hardware DPLL was constructed and tested to provide a comparison to the results obtained from the Markov chain model. In all cases tested, good agreement was found between the theoretical predictions and the experimental data.

Reliability Analysis of the Electrical Control System of Subsea Blowout Preventers Using Markov Models

PubMed Central

Liu, Zengkai; Liu, Yonghong; Cai, Baoping

2014-01-01

Reliability analysis of the electrical control system of a subsea blowout preventer (BOP) stack is carried out based on Markov method. For the subsea BOP electrical control system used in the current work, the 3-2-1-0 and 3-2-0 input voting schemes are available. The effects of the voting schemes on system performance are evaluated based on Markov models. In addition, the effects of failure rates of the modules and repair time on system reliability indices are also investigated. PMID:25409010

Information Entropy Production of Maximum Entropy Markov Chains from Spike Trains

NASA Astrophysics Data System (ADS)

Cofré, Rodrigo; Maldonado, Cesar

2018-01-01

We consider the maximum entropy Markov chain inference approach to characterize the collective statistics of neuronal spike trains, focusing on the statistical properties of the inferred model. We review large deviations techniques useful in this context to describe properties of accuracy and convergence in terms of sampling size. We use these results to study the statistical fluctuation of correlations, distinguishability and irreversibility of maximum entropy Markov chains. We illustrate these applications using simple examples where the large deviation rate function is explicitly obtained for maximum entropy models of relevance in this field.

Bayesian selection of Markov models for symbol sequences: application to microsaccadic eye movements.

PubMed

Bettenbühl, Mario; Rusconi, Marco; Engbert, Ralf; Holschneider, Matthias

2012-01-01

Complex biological dynamics often generate sequences of discrete events which can be described as a Markov process. The order of the underlying Markovian stochastic process is fundamental for characterizing statistical dependencies within sequences. As an example for this class of biological systems, we investigate the Markov order of sequences of microsaccadic eye movements from human observers. We calculate the integrated likelihood of a given sequence for various orders of the Markov process and use this in a Bayesian framework for statistical inference on the Markov order. Our analysis shows that data from most participants are best explained by a first-order Markov process. This is compatible with recent findings of a statistical coupling of subsequent microsaccade orientations. Our method might prove to be useful for a broad class of biological systems.

ASSIST user manual

NASA Technical Reports Server (NTRS)

Johnson, Sally C.; Boerschlein, David P.

1995-01-01

Semi-Markov models can be used to analyze the reliability of virtually any fault-tolerant system. However, the process of delineating all the states and transitions in a complex system model can be devastatingly tedious and error prone. The Abstract Semi-Markov Specification Interface to the SURE Tool (ASSIST) computer program allows the user to describe the semi-Markov model in a high-level language. Instead of listing the individual model states, the user specifies the rules governing the behavior of the system, and these are used to generate the model automatically. A few statements in the abstract language can describe a very large, complex model. Because no assumptions are made about the system being modeled, ASSIST can be used to generate models describing the behavior of any system. The ASSIST program and its input language are described and illustrated by examples.

Generation of intervention strategy for a genetic regulatory network represented by a family of Markov Chains.

PubMed

Berlow, Noah; Pal, Ranadip

2011-01-01

Genetic Regulatory Networks (GRNs) are frequently modeled as Markov Chains providing the transition probabilities of moving from one state of the network to another. The inverse problem of inference of the Markov Chain from noisy and limited experimental data is an ill posed problem and often generates multiple model possibilities instead of a unique one. In this article, we address the issue of intervention in a genetic regulatory network represented by a family of Markov Chains. The purpose of intervention is to alter the steady state probability distribution of the GRN as the steady states are considered to be representative of the phenotypes. We consider robust stationary control policies with best expected behavior. The extreme computational complexity involved in search of robust stationary control policies is mitigated by using a sequential approach to control policy generation and utilizing computationally efficient techniques for updating the stationary probability distribution of a Markov chain following a rank one perturbation.

Simplification of Markov chains with infinite state space and the mathematical theory of random gene expression bursts.

PubMed

Jia, Chen

2017-09-01

Here we develop an effective approach to simplify two-time-scale Markov chains with infinite state spaces by removal of states with fast leaving rates, which improves the simplification method of finite Markov chains. We introduce the concept of fast transition paths and show that the effective transitions of the reduced chain can be represented as the superposition of the direct transitions and the indirect transitions via all the fast transition paths. Furthermore, we apply our simplification approach to the standard Markov model of single-cell stochastic gene expression and provide a mathematical theory of random gene expression bursts. We give the precise mathematical conditions for the bursting kinetics of both mRNAs and proteins. It turns out that random bursts exactly correspond to the fast transition paths of the Markov model. This helps us gain a better understanding of the physics behind the bursting kinetics as an emergent behavior from the fundamental multiscale biochemical reaction kinetics of stochastic gene expression.

Simplification of Markov chains with infinite state space and the mathematical theory of random gene expression bursts

NASA Astrophysics Data System (ADS)

Jia, Chen

2017-09-01

Here we develop an effective approach to simplify two-time-scale Markov chains with infinite state spaces by removal of states with fast leaving rates, which improves the simplification method of finite Markov chains. We introduce the concept of fast transition paths and show that the effective transitions of the reduced chain can be represented as the superposition of the direct transitions and the indirect transitions via all the fast transition paths. Furthermore, we apply our simplification approach to the standard Markov model of single-cell stochastic gene expression and provide a mathematical theory of random gene expression bursts. We give the precise mathematical conditions for the bursting kinetics of both mRNAs and proteins. It turns out that random bursts exactly correspond to the fast transition paths of the Markov model. This helps us gain a better understanding of the physics behind the bursting kinetics as an emergent behavior from the fundamental multiscale biochemical reaction kinetics of stochastic gene expression.

Finding exact constants in a Markov model of Zipfs law generation

NASA Astrophysics Data System (ADS)

Bochkarev, V. V.; Lerner, E. Yu.; Nikiforov, A. A.; Pismenskiy, A. A.

2017-12-01

According to the classical Zipfs law, the word frequency is a power function of the word rank with an exponent -1. The objective of this work is to find multiplicative constant in a Markov model of word generation. Previously, the case of independent letters was mathematically strictly investigated in [Bochkarev V V and Lerner E Yu 2017 International Journal of Mathematics and Mathematical Sciences Article ID 914374]. Unfortunately, the methods used in this paper cannot be generalized in case of Markov chains. The search of the correct formulation of the Markov generalization of this results was performed using experiments with different ergodic matrices of transition probability P. Combinatory technique allowed taking into account all the words with probability of more than e -300 in case of 2 by 2 matrices. It was experimentally proved that the required constant in the limit is equal to the value reciprocal to conditional entropy of matrix row P with weights presenting the elements of the vector π of the stationary distribution of the Markov chain.

Sampling rare fluctuations of discrete-time Markov chains

NASA Astrophysics Data System (ADS)

Whitelam, Stephen

2018-03-01

We describe a simple method that can be used to sample the rare fluctuations of discrete-time Markov chains. We focus on the case of Markov chains with well-defined steady-state measures, and derive expressions for the large-deviation rate functions (and upper bounds on such functions) for dynamical quantities extensive in the length of the Markov chain. We illustrate the method using a series of simple examples, and use it to study the fluctuations of a lattice-based model of active matter that can undergo motility-induced phase separation.

Sampling rare fluctuations of discrete-time Markov chains.

PubMed

Whitelam, Stephen

2018-03-01

We describe a simple method that can be used to sample the rare fluctuations of discrete-time Markov chains. We focus on the case of Markov chains with well-defined steady-state measures, and derive expressions for the large-deviation rate functions (and upper bounds on such functions) for dynamical quantities extensive in the length of the Markov chain. We illustrate the method using a series of simple examples, and use it to study the fluctuations of a lattice-based model of active matter that can undergo motility-induced phase separation.

Hepatitis B screening and vaccination strategies for newly arrived adult Canadian immigrants and refugees: a cost-effectiveness analysis.

PubMed

Rossi, Carmine; Schwartzman, Kevin; Oxlade, Olivia; Klein, Marina B; Greenaway, Chris

2013-01-01

Immigrants have increased mortality from hepatocellular carcinoma as compared to the host populations, primarily due to undetected chronic hepatitis B virus (HBV) infection. Despite this, there are no systematic programs in most immigrant-receiving countries to screen for chronic HBV infection and immigrants are not routinely offered HBV vaccination outside of the universal childhood vaccination program. A cost-effective analysis was performed to compare four HBV screening and vaccination strategies with no intervention in a hypothetical cohort of newly-arriving adult Canadian immigrants. The strategies considered were a) universal vaccination, b) screening for prior immunity and vaccination, c) chronic HBV screening and treatment, and d) combined screening for chronic HBV and prior immunity, treatment and vaccination. The analysis was performed from a societal perspective, using a Markov model. Seroprevalence estimates, annual transition probabilities, health-care costs (in Canadian dollars), and utilities were obtained from the published literature. Acute HBV infection, mortality from chronic HBV, quality-adjusted life years (QALYs), and costs were modeled over the lifetime of the cohort of immigrants. Costs and QALYs were discounted at a rate of 3% per year. Screening for chronic HBV infection, and offering treatment if indicated, was found to be the most cost-effective intervention and was estimated to cost $40,880 per additional QALY gained, relative to no intervention. This strategy was most cost-effective for immigrants < 55 years of age and would cost < $50,000 per additional QALY gained for immigrants from areas where HBV seroprevalence is ≥ 3%. Strategies that included HBV vaccination were either prohibitively expensive or dominated by the chronic HBV screening strategy. Screening for chronic HBV infection from regions where most Canadian immigrants originate, except for Latin America and the Middle East, was found to be reasonably cost-effective and has the potential to reduce HBV-associated morbidity and mortality.

Hepatitis B Screening and Vaccination Strategies for Newly Arrived Adult Canadian Immigrants and Refugees: A Cost-Effectiveness Analysis

PubMed Central

Rossi, Carmine; Schwartzman, Kevin; Oxlade, Olivia; Klein, Marina B.; Greenaway, Chris

2013-01-01

Background Immigrants have increased mortality from hepatocellular carcinoma as compared to the host populations, primarily due to undetected chronic hepatitis B virus (HBV) infection. Despite this, there are no systematic programs in most immigrant-receiving countries to screen for chronic HBV infection and immigrants are not routinely offered HBV vaccination outside of the universal childhood vaccination program. Methods and findings A cost-effective analysis was performed to compare four HBV screening and vaccination strategies with no intervention in a hypothetical cohort of newly-arriving adult Canadian immigrants. The strategies considered were a) universal vaccination, b) screening for prior immunity and vaccination, c) chronic HBV screening and treatment, and d) combined screening for chronic HBV and prior immunity, treatment and vaccination. The analysis was performed from a societal perspective, using a Markov model. Seroprevalence estimates, annual transition probabilities, health-care costs (in Canadian dollars), and utilities were obtained from the published literature. Acute HBV infection, mortality from chronic HBV, quality-adjusted life years (QALYs), and costs were modeled over the lifetime of the cohort of immigrants. Costs and QALYs were discounted at a rate of 3% per year. Screening for chronic HBV infection, and offering treatment if indicated, was found to be the most cost-effective intervention and was estimated to cost $40,880 per additional QALY gained, relative to no intervention. This strategy was most cost-effective for immigrants < 55 years of age and would cost < $50,000 per additional QALY gained for immigrants from areas where HBV seroprevalence is ≥ 3%. Strategies that included HBV vaccination were either prohibitively expensive or dominated by the chronic HBV screening strategy. Conclusions Screening for chronic HBV infection from regions where most Canadian immigrants originate, except for Latin America and the Middle East, was found to be reasonably cost-effective and has the potential to reduce HBV-associated morbidity and mortality. PMID:24205255

Free energies from dynamic weighted histogram analysis using unbiased Markov state model.

PubMed

Rosta, Edina; Hummer, Gerhard

2015-01-13

The weighted histogram analysis method (WHAM) is widely used to obtain accurate free energies from biased molecular simulations. However, WHAM free energies can exhibit significant errors if some of the biasing windows are not fully equilibrated. To account for the lack of full equilibration, we develop the dynamic histogram analysis method (DHAM). DHAM uses a global Markov state model to obtain the free energy along the reaction coordinate. A maximum likelihood estimate of the Markov transition matrix is constructed by joint unbiasing of the transition counts from multiple umbrella-sampling simulations along discretized reaction coordinates. The free energy profile is the stationary distribution of the resulting Markov matrix. For this matrix, we derive an explicit approximation that does not require the usual iterative solution of WHAM. We apply DHAM to model systems, a chemical reaction in water treated using quantum-mechanics/molecular-mechanics (QM/MM) simulations, and the Na(+) ion passage through the membrane-embedded ion channel GLIC. We find that DHAM gives accurate free energies even in cases where WHAM fails. In addition, DHAM provides kinetic information, which we here use to assess the extent of convergence in each of the simulation windows. DHAM may also prove useful in the construction of Markov state models from biased simulations in phase-space regions with otherwise low population.

Detecting critical state before phase transition of complex systems by hidden Markov model

NASA Astrophysics Data System (ADS)

Liu, Rui; Chen, Pei; Li, Yongjun; Chen, Luonan

Identifying the critical state or pre-transition state just before the occurrence of a phase transition is a challenging task, because the state of the system may show little apparent change before this critical transition during the gradual parameter variations. Such dynamics of phase transition is generally composed of three stages, i.e., before-transition state, pre-transition state, and after-transition state, which can be considered as three different Markov processes. Thus, based on this dynamical feature, we present a novel computational method, i.e., hidden Markov model (HMM), to detect the switching point of the two Markov processes from the before-transition state (a stationary Markov process) to the pre-transition state (a time-varying Markov process), thereby identifying the pre-transition state or early-warning signals of the phase transition. To validate the effectiveness, we apply this method to detect the signals of the imminent phase transitions of complex systems based on the simulated datasets, and further identify the pre-transition states as well as their critical modules for three real datasets, i.e., the acute lung injury triggered by phosgene inhalation, MCF-7 human breast cancer caused by heregulin, and HCV-induced dysplasia and hepatocellular carcinoma.

User's Manual MCnest - Markov Chain Nest Productivity Model Version 2.0

EPA Science Inventory

The Markov chain nest productivity model, or MCnest, is a set of algorithms for integrating the results of avian toxicity tests with reproductive life-history data to project the relative magnitude of chemical effects on avian reproduction. The mathematical foundation of MCnest i...

Optimized mixed Markov models for motif identification

PubMed Central

Huang, Weichun; Umbach, David M; Ohler, Uwe; Li, Leping

2006-01-01

Background Identifying functional elements, such as transcriptional factor binding sites, is a fundamental step in reconstructing gene regulatory networks and remains a challenging issue, largely due to limited availability of training samples. Results We introduce a novel and flexible model, the Optimized Mixture Markov model (OMiMa), and related methods to allow adjustment of model complexity for different motifs. In comparison with other leading methods, OMiMa can incorporate more than the NNSplice's pairwise dependencies; OMiMa avoids model over-fitting better than the Permuted Variable Length Markov Model (PVLMM); and OMiMa requires smaller training samples than the Maximum Entropy Model (MEM). Testing on both simulated and actual data (regulatory cis-elements and splice sites), we found OMiMa's performance superior to the other leading methods in terms of prediction accuracy, required size of training data or computational time. Our OMiMa system, to our knowledge, is the only motif finding tool that incorporates automatic selection of the best model. OMiMa is freely available at [1]. Conclusion Our optimized mixture of Markov models represents an alternative to the existing methods for modeling dependent structures within a biological motif. Our model is conceptually simple and effective, and can improve prediction accuracy and/or computational speed over other leading methods. PMID:16749929

Recovery of Graded Response Model Parameters: A Comparison of Marginal Maximum Likelihood and Markov Chain Monte Carlo Estimation

ERIC Educational Resources Information Center

Kieftenbeld, Vincent; Natesan, Prathiba

2012-01-01

Markov chain Monte Carlo (MCMC) methods enable a fully Bayesian approach to parameter estimation of item response models. In this simulation study, the authors compared the recovery of graded response model parameters using marginal maximum likelihood (MML) and Gibbs sampling (MCMC) under various latent trait distributions, test lengths, and…

Surgical motion characterization in simulated needle insertion procedures

NASA Astrophysics Data System (ADS)

Holden, Matthew S.; Ungi, Tamas; Sargent, Derek; McGraw, Robert C.; Fichtinger, Gabor

2012-02-01

PURPOSE: Evaluation of surgical performance in image-guided needle insertions is of emerging interest, to both promote patient safety and improve the efficiency and effectiveness of training. The purpose of this study was to determine if a Markov model-based algorithm can more accurately segment a needle-based surgical procedure into its five constituent tasks than a simple threshold-based algorithm. METHODS: Simulated needle trajectories were generated with known ground truth segmentation by a synthetic procedural data generator, with random noise added to each degree of freedom of motion. The respective learning algorithms were trained, and then tested on different procedures to determine task segmentation accuracy. In the threshold-based algorithm, a change in tasks was detected when the needle crossed a position/velocity threshold. In the Markov model-based algorithm, task segmentation was performed by identifying the sequence of Markov models most likely to have produced the series of observations. RESULTS: For amplitudes of translational noise greater than 0.01mm, the Markov model-based algorithm was significantly more accurate in task segmentation than the threshold-based algorithm (82.3% vs. 49.9%, p<0.001 for amplitude 10.0mm). For amplitudes less than 0.01mm, the two algorithms produced insignificantly different results. CONCLUSION: Task segmentation of simulated needle insertion procedures was improved by using a Markov model-based algorithm as opposed to a threshold-based algorithm for procedures involving translational noise.

Markovian Interpretations of Dual Retrieval Processes

PubMed Central

Gomes, C. F. A.; Nakamura, K.; Reyna, V. F.

2013-01-01

A half-century ago, at the dawn of the all-or-none learning era, Estes showed that finite Markov chains supply a tractable, comprehensive framework for discrete-change data of the sort that he envisioned for shifts in conditioning states in stimulus sampling theory. Shortly thereafter, such data rapidly accumulated in many spheres of human learning and animal conditioning, and Estes’ work stimulated vigorous development of Markov models to handle them. A key outcome was that the data of the workhorse paradigms of episodic memory, recognition and recall, proved to be one- and two-stage Markovian, respectively, to close approximations. Subsequently, Markov modeling of recognition and recall all but disappeared from the literature, but it is now reemerging in the wake of dual-process conceptions of episodic memory. In recall, in particular, Markov models are being used to measure two retrieval operations (direct access and reconstruction) and a slave familiarity operation. In the present paper, we develop this family of models and present the requisite machinery for fit evaluation and significance testing. Results are reviewed from selected experiments in which the recall models were used to understand dual memory processes. PMID:24948840

Prediction and generation of binary Markov processes: Can a finite-state fox catch a Markov mouse?

NASA Astrophysics Data System (ADS)

Ruebeck, Joshua B.; James, Ryan G.; Mahoney, John R.; Crutchfield, James P.

2018-01-01

Understanding the generative mechanism of a natural system is a vital component of the scientific method. Here, we investigate one of the fundamental steps toward this goal by presenting the minimal generator of an arbitrary binary Markov process. This is a class of processes whose predictive model is well known. Surprisingly, the generative model requires three distinct topologies for different regions of parameter space. We show that a previously proposed generator for a particular set of binary Markov processes is, in fact, not minimal. Our results shed the first quantitative light on the relative (minimal) costs of prediction and generation. We find, for instance, that the difference between prediction and generation is maximized when the process is approximately independently, identically distributed.

Markov Mixed Effects Modeling Using Electronic Adherence Monitoring Records Identifies Influential Covariates to HIV Preexposure Prophylaxis.

PubMed

Madrasi, Kumpal; Chaturvedula, Ayyappa; Haberer, Jessica E; Sale, Mark; Fossler, Michael J; Bangsberg, David; Baeten, Jared M; Celum, Connie; Hendrix, Craig W

2017-05-01

Adherence is a major factor in the effectiveness of preexposure prophylaxis (PrEP) for HIV prevention. Modeling patterns of adherence helps to identify influential covariates of different types of adherence as well as to enable clinical trial simulation so that appropriate interventions can be developed. We developed a Markov mixed-effects model to understand the covariates influencing adherence patterns to daily oral PrEP. Electronic adherence records (date and time of medication bottle cap opening) from the Partners PrEP ancillary adherence study with a total of 1147 subjects were used. This study included once-daily dosing regimens of placebo, oral tenofovir disoproxil fumarate (TDF), and TDF in combination with emtricitabine (FTC), administered to HIV-uninfected members of serodiscordant couples. One-coin and first- to third-order Markov models were fit to the data using NONMEM ® 7.2. Model selection criteria included objective function value (OFV), Akaike information criterion (AIC), visual predictive checks, and posterior predictive checks. Covariates were included based on forward addition (α = 0.05) and backward elimination (α = 0.001). Markov models better described the data than 1-coin models. A third-order Markov model gave the lowest OFV and AIC, but the simpler first-order model was used for covariate model building because no additional benefit on prediction of target measures was observed for higher-order models. Female sex and older age had a positive impact on adherence, whereas Sundays, sexual abstinence, and sex with a partner other than the study partner had a negative impact on adherence. Our findings suggest adherence interventions should consider the role of these factors. © 2016, The American College of Clinical Pharmacology.

An abstract specification language for Markov reliability models

NASA Technical Reports Server (NTRS)

Butler, R. W.

1985-01-01

Markov models can be used to compute the reliability of virtually any fault tolerant system. However, the process of delineating all of the states and transitions in a model of complex system can be devastatingly tedious and error-prone. An approach to this problem is presented utilizing an abstract model definition language. This high level language is described in a nonformal manner and illustrated by example.

An abstract language for specifying Markov reliability models

NASA Technical Reports Server (NTRS)

Butler, Ricky W.

1986-01-01

Markov models can be used to compute the reliability of virtually any fault tolerant system. However, the process of delineating all of the states and transitions in a model of complex system can be devastatingly tedious and error-prone. An approach to this problem is presented utilizing an abstract model definition language. This high level language is described in a nonformal manner and illustrated by example.

Avian life history profiles for use in the Markov chain nest productivity model (MCnest)

EPA Science Inventory

The Markov Chain nest productivity model, or MCnest, quantitatively estimates the effects of pesticides or other toxic chemicals on annual reproductive success of avian species (Bennett and Etterson 2013, Etterson and Bennett 2013). The Basic Version of MCnest was developed as a...

An empirical comparison of Markov cohort modeling and discrete event simulation in a capacity-constrained health care setting.

PubMed

Standfield, L B; Comans, T A; Scuffham, P A

2017-01-01

To empirically compare Markov cohort modeling (MM) and discrete event simulation (DES) with and without dynamic queuing (DQ) for cost-effectiveness (CE) analysis of a novel method of health services delivery where capacity constraints predominate. A common data-set comparing usual orthopedic care (UC) to an orthopedic physiotherapy screening clinic and multidisciplinary treatment service (OPSC) was used to develop a MM and a DES without (DES-no-DQ) and with DQ (DES-DQ). Model results were then compared in detail. The MM predicted an incremental CE ratio (ICER) of $495 per additional quality-adjusted life-year (QALY) for OPSC over UC. The DES-no-DQ showed OPSC dominating UC; the DES-DQ generated an ICER of $2342 per QALY. The MM and DES-no-DQ ICER estimates differed due to the MM having implicit delays built into its structure as a result of having fixed cycle lengths, which are not a feature of DES. The non-DQ models assume that queues are at a steady state. Conversely, queues in the DES-DQ develop flexibly with supply and demand for resources, in this case, leading to different estimates of resource use and CE. The choice of MM or DES (with or without DQ) would not alter the reimbursement of OPSC as it was highly cost-effective compared to UC in all analyses. However, the modeling method may influence decisions where ICERs are closer to the CE acceptability threshold, or where capacity constraints and DQ are important features of the system. In these cases, DES-DQ would be the preferred modeling technique to avoid incorrect resource allocation decisions.

HIPPI: highly accurate protein family classification with ensembles of HMMs.

PubMed

Nguyen, Nam-Phuong; Nute, Michael; Mirarab, Siavash; Warnow, Tandy

2016-11-11

Given a new biological sequence, detecting membership in a known family is a basic step in many bioinformatics analyses, with applications to protein structure and function prediction and metagenomic taxon identification and abundance profiling, among others. Yet family identification of sequences that are distantly related to sequences in public databases or that are fragmentary remains one of the more difficult analytical problems in bioinformatics. We present a new technique for family identification called HIPPI (Hierarchical Profile Hidden Markov Models for Protein family Identification). HIPPI uses a novel technique to represent a multiple sequence alignment for a given protein family or superfamily by an ensemble of profile hidden Markov models computed using HMMER. An evaluation of HIPPI on the Pfam database shows that HIPPI has better overall precision and recall than blastp, HMMER, and pipelines based on HHsearch, and maintains good accuracy even for fragmentary query sequences and for protein families with low average pairwise sequence identity, both conditions where other methods degrade in accuracy. HIPPI provides accurate protein family identification and is robust to difficult model conditions. Our results, combined with observations from previous studies, show that ensembles of profile Hidden Markov models can better represent multiple sequence alignments than a single profile Hidden Markov model, and thus can improve downstream analyses for various bioinformatic tasks. Further research is needed to determine the best practices for building the ensemble of profile Hidden Markov models. HIPPI is available on GitHub at https://github.com/smirarab/sepp .

A big-data model for multi-modal public transportation with application to macroscopic control and optimisation

NASA Astrophysics Data System (ADS)

Faizrahnemoon, Mahsa; Schlote, Arieh; Maggi, Lorenzo; Crisostomi, Emanuele; Shorten, Robert

2015-11-01

This paper describes a Markov-chain-based approach to modelling multi-modal transportation networks. An advantage of the model is the ability to accommodate complex dynamics and handle huge amounts of data. The transition matrix of the Markov chain is built and the model is validated using the data extracted from a traffic simulator. A realistic test-case using multi-modal data from the city of London is given to further support the ability of the proposed methodology to handle big quantities of data. Then, we use the Markov chain as a control tool to improve the overall efficiency of a transportation network, and some practical examples are described to illustrate the potentials of the approach.

Gaze patterns hold key to unlocking successful search strategies and increasing polyp detection rate in colonoscopy.

PubMed

Lami, Mariam; Singh, Harsimrat; Dilley, James H; Ashraf, Hajra; Edmondon, Matthew; Orihuela-Espina, Felipe; Hoare, Jonathan; Darzi, Ara; Sodergren, Mikael H

2018-02-07

The adenoma detection rate (ADR) is an important quality indicator in colonoscopy. The aim of this study was to evaluate the changes in visual gaze patterns (VGPs) with increasing polyp detection rate (PDR), a surrogate marker of ADR. 18 endoscopists participated in the study. VGPs were measured using eye-tracking technology during the withdrawal phase of colonoscopy. VGPs were characterized using two analyses - screen and anatomy. Eye-tracking parameters were used to characterize performance, which was further substantiated using hidden Markov model (HMM) analysis. Subjects with higher PDRs spent more time viewing the outer ring of the 3 × 3 grid for both analyses (screen-based: r = 0.56, P  = 0.02; anatomy: r = 0.62, P  < 0.01). Fixation distribution to the "bottom U" of the screen in screen-based analysis was positively correlated with PDR (r = 0.62, P  = 0.01). HMM demarcated the VGPs into three PDR groups. This study defined distinct VGPs that are associated with expert behavior. These data may allow introduction of visual gaze training within structured training programs, and have implications for adoption in higher-level assessment. © Georg Thieme Verlag KG Stuttgart · New York.

Inferring Markov chains: Bayesian estimation, model comparison, entropy rate, and out-of-class modeling.

PubMed

Strelioff, Christopher C; Crutchfield, James P; Hübler, Alfred W

2007-07-01

Markov chains are a natural and well understood tool for describing one-dimensional patterns in time or space. We show how to infer kth order Markov chains, for arbitrary k , from finite data by applying Bayesian methods to both parameter estimation and model-order selection. Extending existing results for multinomial models of discrete data, we connect inference to statistical mechanics through information-theoretic (type theory) techniques. We establish a direct relationship between Bayesian evidence and the partition function which allows for straightforward calculation of the expectation and variance of the conditional relative entropy and the source entropy rate. Finally, we introduce a method that uses finite data-size scaling with model-order comparison to infer the structure of out-of-class processes.

A hierarchical approach to reliability modeling of fault-tolerant systems. M.S. Thesis

NASA Technical Reports Server (NTRS)

Gossman, W. E.

1986-01-01

A methodology for performing fault tolerant system reliability analysis is presented. The method decomposes a system into its subsystems, evaluates vent rates derived from the subsystem's conditional state probability vector and incorporates those results into a hierarchical Markov model of the system. This is done in a manner that addresses failure sequence dependence associated with the system's redundancy management strategy. The method is derived for application to a specific system definition. Results are presented that compare the hierarchical model's unreliability prediction to that of a more complicated tandard Markov model of the system. The results for the example given indicate that the hierarchical method predicts system unreliability to a desirable level of accuracy while achieving significant computational savings relative to component level Markov model of the system.

Preliminary testing for the Markov property of the fifteen chromatin states of the Broad Histone Track.

PubMed

Lee, Kyung-Eun; Park, Hyun-Seok

2015-01-01

Epigenetic computational analyses based on Markov chains can integrate dependencies between regions in the genome that are directly adjacent. In this paper, the BED files of fifteen chromatin states of the Broad Histone Track of the ENCODE project are parsed, and comparative nucleotide frequencies of regional chromatin blocks are thoroughly analyzed to detect the Markov property in them. We perform various tests to examine the Markov property embedded in a frequency domain by checking for the presence of the Markov property in the various chromatin states. We apply these tests to each region of the fifteen chromatin states. The results of our simulation indicate that some of the chromatin states possess a stronger Markov property than others. We discuss the significance of our findings in statistical models of nucleotide sequences that are necessary for the computational analysis of functional units in noncoding DNA.

Entanglement revival can occur only when the system-environment state is not a Markov state

NASA Astrophysics Data System (ADS)

Sargolzahi, Iman

2018-06-01

Markov states have been defined for tripartite quantum systems. In this paper, we generalize the definition of the Markov states to arbitrary multipartite case and find the general structure of an important subset of them, which we will call strong Markov states. In addition, we focus on an important property of the Markov states: If the initial state of the whole system-environment is a Markov state, then each localized dynamics of the whole system-environment reduces to a localized subdynamics of the system. This provides us a necessary condition for entanglement revival in an open quantum system: Entanglement revival can occur only when the system-environment state is not a Markov state. To illustrate (a part of) our results, we consider the case that the environment is modeled as classical. In this case, though the correlation between the system and the environment remains classical during the evolution, the change of the state of the system-environment, from its initial Markov state to a state which is not a Markov one, leads to the entanglement revival in the system. This shows that the non-Markovianity of a state is not equivalent to the existence of non-classical correlation in it, in general.

Using resource modelling to inform decision making and service planning: the case of colorectal cancer screening in Ireland.

PubMed

Sharp, Linda; Tilson, Lesley; Whyte, Sophie; Ceilleachair, Alan O; Walsh, Cathal; Usher, Cara; Tappenden, Paul; Chilcott, James; Staines, Anthony; Barry, Michael; Comber, Harry

2013-03-19

Organised colorectal cancer screening is likely to be cost-effective, but cost-effectiveness results alone may not help policy makers to make decisions about programme feasibility or service providers to plan programme delivery. For these purposes, estimates of the impact on the health services of actually introducing screening in the target population would be helpful. However, these types of analyses are rarely reported. As an illustration of such an approach, we estimated annual health service resource requirements and health outcomes over the first decade of a population-based colorectal cancer screening programme in Ireland. A Markov state-transition model of colorectal neoplasia natural history was used. Three core screening scenarios were considered: (a) flexible sigmoidoscopy (FSIG) once at age 60, (b) biennial guaiac-based faecal occult blood tests (gFOBT) at 55-74 years, and (c) biennial faecal immunochemical tests (FIT) at 55-74 years. Three alternative FIT roll-out scenarios were also investigated relating to age-restricted screening (55-64 years) and staggered age-based roll-out across the 55-74 age group. Parameter estimates were derived from literature review, existing screening programmes, and expert opinion. Results were expressed in relation to the 2008 population (4.4 million people, of whom 700,800 were aged 55-74). FIT-based screening would deliver the greatest health benefits, averting 164 colorectal cancer cases and 272 deaths in year 10 of the programme. Capacity would be required for 11,095-14,820 diagnostic and surveillance colonoscopies annually, compared to 381-1,053 with FSIG-based, and 967-1,300 with gFOBT-based, screening. With FIT, in year 10, these colonoscopies would result in 62 hospital admissions for abdominal bleeding, 27 bowel perforations and one death. Resource requirements for pathology, diagnostic radiology, radiotherapy and colorectal resection were highest for FIT. Estimates depended on screening uptake. Alternative FIT roll-out scenarios had lower resource requirements. While FIT-based screening would quite quickly generate attractive health outcomes, it has heavy resource requirements. These could impact on the feasibility of a programme based on this screening modality. Staggered age-based roll-out would allow time to increase endoscopy capacity to meet programme requirements. Resource modelling of this type complements conventional cost-effectiveness analyses and can help inform policy making and service planning.

Cost-effectiveness of the faecal immunochemical test at a range of positivity thresholds compared with the guaiac faecal occult blood test in the NHS Bowel Cancer Screening Programme in England

PubMed Central

Halloran, Stephen

2017-01-01

Objectives Through the National Health Service (NHS) Bowel Cancer Screening Programme (BCSP), men and women in England aged between 60 and 74 years are invited for colorectal cancer (CRC) screening every 2 years using the guaiac faecal occult blood test (gFOBT). The aim of this analysis was to estimate the cost–utility of the faecal immunochemical test for haemoglobin (FIT) compared with gFOBT for a cohort beginning screening aged 60 years at a range of FIT positivity thresholds. Design We constructed a cohort-based Markov state transition model of CRC disease progression and screening. Screening uptake, detection, adverse event, mortality and cost data were taken from BCSP data and national sources, including a recent large pilot study of FIT screening in the BCSP. Results Our results suggest that FIT is cost-effective compared with gFOBT at all thresholds, resulting in cost savings and quality-adjusted life years (QALYs) gained over a lifetime time horizon. FIT was cost-saving (p<0.001) and resulted in QALY gains of 0.014 (95% CI 0.012 to 0.017) at the base case threshold of 180 µg Hb/g faeces. Greater health gains and cost savings were achieved as the FIT threshold was decreased due to savings in cancer management costs. However, at lower thresholds, FIT was also associated with more colonoscopies (increasing from 32 additional colonoscopies per 1000 people invited for screening for FIT 180 µg Hb/g faeces to 421 additional colonoscopies per 1000 people invited for screening for FIT 20 µg Hb/g faeces over a 40-year time horizon). Parameter uncertainty had limited impact on the conclusions. Conclusions This is the first published economic analysis of FIT screening in England using data directly comparing FIT with gFOBT in the NHS BSCP. These results for a cohort starting screening aged 60 years suggest that FIT is highly cost-effective at all thresholds considered. Further modelling is needed to estimate economic outcomes for screening across all age cohorts simultaneously. PMID:29079605

Real-time antenna fault diagnosis experiments at DSS 13

NASA Technical Reports Server (NTRS)

Mellstrom, J.; Pierson, C.; Smyth, P.

1992-01-01

Experimental results obtained when a previously described fault diagnosis system was run online in real time at the 34-m beam waveguide antenna at Deep Space Station (DSS) 13 are described. Experimental conditions and the quality of results are described. A neural network model and a maximum-likelihood Gaussian classifier are compared with and without a Markov component to model temporal context. At the rate of a state update every 6.4 seconds, over a period of roughly 1 hour, the neural-Markov system had zero errors (incorrect state estimates) while monitoring both faulty and normal operations. The overall results indicate that the neural-Markov combination is the most accurate model and has significant practical potential.

The application of a Grey Markov Model to forecasting annual maximum water levels at hydrological stations

NASA Astrophysics Data System (ADS)

Dong, Sheng; Chi, Kun; Zhang, Qiyi; Zhang, Xiangdong

2012-03-01

Compared with traditional real-time forecasting, this paper proposes a Grey Markov Model (GMM) to forecast the maximum water levels at hydrological stations in the estuary area. The GMM combines the Grey System and Markov theory into a higher precision model. The GMM takes advantage of the Grey System to predict the trend values and uses the Markov theory to forecast fluctuation values, and thus gives forecast results involving two aspects of information. The procedure for forecasting annul maximum water levels with the GMM contains five main steps: 1) establish the GM (1, 1) model based on the data series; 2) estimate the trend values; 3) establish a Markov Model based on relative error series; 4) modify the relative errors caused in step 2, and then obtain the relative errors of the second order estimation; 5) compare the results with measured data and estimate the accuracy. The historical water level records (from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River near Tianjin, China are utilized to calibrate and verify the proposed model according to the above steps. Every 25 years' data are regarded as a hydro-sequence. Eight groups of simulated results show reasonable agreement between the predicted values and the measured data. The GMM is also applied to the 10 other hydrological stations in the same estuary. The forecast results for all of the hydrological stations are good or acceptable. The feasibility and effectiveness of this new forecasting model have been proved in this paper.

[Cost-effectiveness of the HIV screening program carried out in Guangxi Zhuang Autonomous Region infectious disease special demonstration project areas].

PubMed

Lu, Huaxiang; Luo, Liuhong; Chen, Li; Zhang, Shizhen; Liang, Yingfang; Li, Li; Chen, Zhenqiang; Huo, Xiaoxing; Wu, Xinghua

2015-06-01

To analyze the cost effectiveness of HIV screening project in three Guangxi infectious disease special demonstration project countries in 2013. To calculate the funds used for the HIV screening project and to study the data on HIV/AIDS and HAART. A five-tree markov model was used to evaluate the quality adjusted life year (QALY) of this HIV screening project and to analyze the related cost effectiveness of the project. The cost of HIV screening in Guangxi infectious disease special demonstration project areas was 19.205 million Yuan and having identified 1 218 HIV/AIDS patients. The average costs for HIV/AIDS positive detection in three project countries were 14.562, 18.424 and 14.042 thousand Yuan per case. The QALYs gained from finding a HIV/AIDS case were 12.736, 8.523 and 8.321 on average, with the total number of QALYs gained from the project as 5 973.184, 3 613.752 and 2 704.325. The overall cost effectiveness ratio of the project was 1.562 thousand Yuan per QALY, and 1.143, 2.162 and 1.688 thousand Yuan per QALY in these three project countries. Project country "A" showed better cost effectiveness index than country B and C. The HIV screening project in Guangxi seemed relatively cost-effective but the average cost of HIV/AIDS positive detection was expensive. To strengthen HAART work for HIV/AIDS could improve the cost-effective of the project.

The reading of components of diabetic retinopathy: an evolutionary approach for filtering normal digital fundus imaging in screening and population based studies.

PubMed

Tang, Hongying Lilian; Goh, Jonathan; Peto, Tunde; Ling, Bingo Wing-Kuen; Al Turk, Lutfiah Ismail; Hu, Yin; Wang, Su; Saleh, George Michael

2013-01-01

In any diabetic retinopathy screening program, about two-thirds of patients have no retinopathy. However, on average, it takes a human expert about one and a half times longer to decide an image is normal than to recognize an abnormal case with obvious features. In this work, we present an automated system for filtering out normal cases to facilitate a more effective use of grading time. The key aim with any such tool is to achieve high sensitivity and specificity to ensure patients' safety and service efficiency. There are many challenges to overcome, given the variation of images and characteristics to identify. The system combines computed evidence obtained from various processing stages, including segmentation of candidate regions, classification and contextual analysis through Hidden Markov Models. Furthermore, evolutionary algorithms are employed to optimize the Hidden Markov Models, feature selection and heterogeneous ensemble classifiers. In order to evaluate its capability of identifying normal images across diverse populations, a population-oriented study was undertaken comparing the software's output to grading by humans. In addition, population based studies collect large numbers of images on subjects expected to have no abnormality. These studies expect timely and cost-effective grading. Altogether 9954 previously unseen images taken from various populations were tested. All test images were masked so the automated system had not been exposed to them before. This system was trained using image subregions taken from about 400 sample images. Sensitivities of 92.2% and specificities of 90.4% were achieved varying between populations and population clusters. Of all images the automated system decided to be normal, 98.2% were true normal when compared to the manual grading results. These results demonstrate scalability and strong potential of such an integrated computational intelligence system as an effective tool to assist a grading service.

zipHMMlib: a highly optimised HMM library exploiting repetitions in the input to speed up the forward algorithm.

PubMed

Sand, Andreas; Kristiansen, Martin; Pedersen, Christian N S; Mailund, Thomas

2013-11-22

Hidden Markov models are widely used for genome analysis as they combine ease of modelling with efficient analysis algorithms. Calculating the likelihood of a model using the forward algorithm has worst case time complexity linear in the length of the sequence and quadratic in the number of states in the model. For genome analysis, however, the length runs to millions or billions of observations, and when maximising the likelihood hundreds of evaluations are often needed. A time efficient forward algorithm is therefore a key ingredient in an efficient hidden Markov model library. We have built a software library for efficiently computing the likelihood of a hidden Markov model. The library exploits commonly occurring substrings in the input to reuse computations in the forward algorithm. In a pre-processing step our library identifies common substrings and builds a structure over the computations in the forward algorithm which can be reused. This analysis can be saved between uses of the library and is independent of concrete hidden Markov models so one preprocessing can be used to run a number of different models.Using this library, we achieve up to 78 times shorter wall-clock time for realistic whole-genome analyses with a real and reasonably complex hidden Markov model. In one particular case the analysis was performed in less than 8 minutes compared to 9.6 hours for the previously fastest library. We have implemented the preprocessing procedure and forward algorithm as a C++ library, zipHMM, with Python bindings for use in scripts. The library is available at http://birc.au.dk/software/ziphmm/.

Markov Chain Models for Stochastic Behavior in Resonance Overlap Regions

NASA Astrophysics Data System (ADS)

McCarthy, Morgan; Quillen, Alice

2018-01-01

We aim to predict lifetimes of particles in chaotic zoneswhere resonances overlap. A continuous-time Markov chain model isconstructed using mean motion resonance libration timescales toestimate transition times between resonances. The model is applied todiffusion in the co-rotation region of a planet. For particles begunat low eccentricity, the model is effective for early diffusion, butnot at later time when particles experience close encounters to the planet.

Modeling the Distribution of Fingerprint Characteristics. Revision 1.

DTIC Science & Technology

1980-09-19

the details of the print. The ridge-line details are termed Galton characteristics since Sir Francis Galton was among the first to study them...U.S.A. CONTENTS Abstract 1. Introduction 2. Background Information on Fingerprints 2.1. Types 2.2. Ridge counts 2.3. The Galton details 3. Data...The Multinomial Markov Model 7. The Poisson Markov Model 8. The Infinitely Divisible Model Acknowledgements References Appendices A The Galton

Multivariate generalized hidden Markov regression models with random covariates: Physical exercise in an elderly population.

PubMed

Punzo, Antonio; Ingrassia, Salvatore; Maruotti, Antonello

2018-04-22

A time-varying latent variable model is proposed to jointly analyze multivariate mixed-support longitudinal data. The proposal can be viewed as an extension of hidden Markov regression models with fixed covariates (HMRMFCs), which is the state of the art for modelling longitudinal data, with a special focus on the underlying clustering structure. HMRMFCs are inadequate for applications in which a clustering structure can be identified in the distribution of the covariates, as the clustering is independent from the covariates distribution. Here, hidden Markov regression models with random covariates are introduced by explicitly specifying state-specific distributions for the covariates, with the aim of improving the recovering of the clusters in the data with respect to a fixed covariates paradigm. The hidden Markov regression models with random covariates class is defined focusing on the exponential family, in a generalized linear model framework. Model identifiability conditions are sketched, an expectation-maximization algorithm is outlined for parameter estimation, and various implementation and operational issues are discussed. Properties of the estimators of the regression coefficients, as well as of the hidden path parameters, are evaluated through simulation experiments and compared with those of HMRMFCs. The method is applied to physical activity data. Copyright © 2018 John Wiley & Sons, Ltd.

Reducing Periconceptional Methylmercury Exposure: Cost–Utility Analysis for a Proposed Screening Program for Women Planning a Pregnancy in Ontario, Canada

PubMed Central

Rennie, Colin; Coyle, Doug

2015-01-01

Background The assessment of neurodevelopmental effects in children associated with prenatal methylmercury exposure, from contaminated fish and seafood in the maternal diet, has recently been strengthened by adjustment for the negative confounding resulting from co-exposure to beneficial polyunsaturated fatty acids (PUFAs). Objectives We aimed to determine the cost-effectiveness of a periconceptional screening program of blood mercury concentration for women planning to become pregnant in Ontario, Canada. Fish intake recommendations would be provided for those found to have blood mercury levels above the intervention threshold. Methods Analysis was conducted using a combined decision tree/Markov model to compare the proposed screening intervention with standard care from a societal perspective over a lifetime horizon. We used the national blood mercury distributions of women 20–49 years of age reported in the Canadian Health Measures Survey from 2009 through 2011 to determine the cognitive deficits associated with prenatal methylmercury exposure for successful planned pregnancies. Outcomes modeled included the loss in quality of life and the remedial education costs. Value of information analysis was conducted to assess the underlying uncertainty around the model results and to identify which parameters contribute most to this uncertainty. Results The incremental cost per quality-adjusted life year (QALY) gained for the proposed screening intervention was estimated to be Can$18,051, and the expected value for a willingness to pay of Can$50,000/QALY to be Can$0.61. Conclusions Our findings suggest that the proposed periconceptional blood mercury screening program for women planning a pregnancy would be highly cost-effective from a societal perspective. The results of a value of information analysis confirm the robustness of the study’s conclusions. Citation Gaskin J, Rennie C, Coyle D. 2015. Reducing periconceptional methylmercury exposure: cost–utility analysis for a proposed screening program for women planning a pregnancy in Ontario, Canada. Environ Health Perspect 123:1337–1344; http://dx.doi.org/10.1289/ehp.1409034 PMID:26024213

Image segmentation using hidden Markov Gauss mixture models.

PubMed

Pyun, Kyungsuk; Lim, Johan; Won, Chee Sun; Gray, Robert M

2007-07-01

Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. We develop a multiclass image segmentation method using hidden Markov Gauss mixture models (HMGMMs) and provide examples of segmentation of aerial images and textures. HMGMMs incorporate supervised learning, fitting the observation probability distribution given each class by a Gauss mixture estimated using vector quantization with a minimum discrimination information (MDI) distortion. We formulate the image segmentation problem using a maximum a posteriori criteria and find the hidden states that maximize the posterior density given the observation. We estimate both the hidden Markov parameter and hidden states using a stochastic expectation-maximization algorithm. Our results demonstrate that HMGMM provides better classification in terms of Bayes risk and spatial homogeneity of the classified objects than do several popular methods, including classification and regression trees, learning vector quantization, causal hidden Markov models (HMMs), and multiresolution HMMs. The computational load of HMGMM is similar to that of the causal HMM.

Evaluation of Usability Utilizing Markov Models

ERIC Educational Resources Information Center

Penedo, Janaina Rodrigues; Diniz, Morganna; Ferreira, Simone Bacellar Leal; Silveira, Denis S.; Capra, Eliane

2012-01-01

Purpose: The purpose of this paper is to analyze the usability of a remote learning system in its initial development phase, using a quantitative usability evaluation method through Markov models. Design/methodology/approach: The paper opted for an exploratory study. The data of interest of the research correspond to the possible accesses of users…

A Test of the Need Hierarchy Concept by a Markov Model of Change in Need Strength.

ERIC Educational Resources Information Center

Rauschenberger, John; And Others

1980-01-01

In this study of 547 high school graduates, Alderfer's and Maslow's need hierarchy theories were expressed in Markov chain form and were subjected to empirical test. Both models were disconfirmed. Corroborative multiwave correlational analysis also failed to support the need hierarchy concept. (Author/IRT)

Metadynamics Enhanced Markov Modeling of Protein Dynamics.

PubMed

Biswas, Mithun; Lickert, Benjamin; Stock, Gerhard

2018-05-31

Enhanced sampling techniques represent a versatile approach to account for rare conformational transitions in biomolecules. A particularly promising strategy is to combine massive parallel computing of short molecular dynamics (MD) trajectories (to sample the free energy landscape of the system) with Markov state modeling (to rebuild the kinetics from the sampled data). To obtain well-distributed initial structures for the short trajectories, it is proposed to employ metadynamics MD, which quickly sweeps through the entire free energy landscape of interest. Being only used to generate initial conformations, the implementation of metadynamics can be simple and fast. The conformational dynamics of helical peptide Aib 9 is adopted to discuss various technical issues of the approach, including metadynamics settings, minimal number and length of short MD trajectories, and the validation of the resulting Markov models. Using metadynamics to launch some thousands of nanosecond trajectories, several Markov state models are constructed that reveal that previous unbiased MD simulations of in total 16 μs length cannot provide correct equilibrium populations or qualitative features of the pathway distribution of the short peptide.

Measurement-based reliability/performability models

NASA Technical Reports Server (NTRS)

Hsueh, Mei-Chen

1987-01-01

Measurement-based models based on real error-data collected on a multiprocessor system are described. Model development from the raw error-data to the estimation of cumulative reward is also described. A workload/reliability model is developed based on low-level error and resource usage data collected on an IBM 3081 system during its normal operation in order to evaluate the resource usage/error/recovery process in a large mainframe system. Thus, both normal and erroneous behavior of the system are modeled. The results provide an understanding of the different types of errors and recovery processes. The measured data show that the holding times in key operational and error states are not simple exponentials and that a semi-Markov process is necessary to model the system behavior. A sensitivity analysis is performed to investigate the significance of using a semi-Markov process, as opposed to a Markov process, to model the measured system.

Operations and support cost modeling using Markov chains

NASA Technical Reports Server (NTRS)

Unal, Resit

1989-01-01

Systems for future missions will be selected with life cycle costs (LCC) as a primary evaluation criterion. This reflects the current realization that only systems which are considered affordable will be built in the future due to the national budget constaints. Such an environment calls for innovative cost modeling techniques which address all of the phases a space system goes through during its life cycle, namely: design and development, fabrication, operations and support; and retirement. A significant portion of the LCC for reusable systems are generated during the operations and support phase (OS). Typically, OS costs can account for 60 to 80 percent of the total LCC. Clearly, OS costs are wholly determined or at least strongly influenced by decisions made during the design and development phases of the project. As a result OS costs need to be considered and estimated early in the conceptual phase. To be effective, an OS cost estimating model needs to account for actual instead of ideal processes by associating cost elements with probabilities. One approach that may be suitable for OS cost modeling is the use of the Markov Chain Process. Markov chains are an important method of probabilistic analysis for operations research analysts but they are rarely used for life cycle cost analysis. This research effort evaluates the use of Markov Chains in LCC analysis by developing OS cost model for a hypothetical reusable space transportation vehicle (HSTV) and suggests further uses of the Markov Chain process as a design-aid tool.

Predicted impact of extending the screening interval for diabetic retinopathy: the Scottish Diabetic Retinopathy Screening programme.

PubMed

Looker, H C; Nyangoma, S O; Cromie, D T; Olson, J A; Leese, G P; Philip, S; Black, M W; Doig, J; Lee, N; Briggs, A; Hothersall, E J; Morris, A D; Lindsay, R S; McKnight, J A; Pearson, D W M; Sattar, N A; Wild, S H; McKeigue, P; Colhoun, H M

2013-08-01

The aim of our study was to identify subgroups of patients attending the Scottish Diabetic Retinopathy Screening (DRS) programme who might safely move from annual to two yearly retinopathy screening. This was a retrospective cohort study of screening data from the DRS programme collected between 2005 and 2011 for people aged ≥12 years with type 1 or type 2 diabetes in Scotland. We used hidden Markov models to calculate the probabilities of transitions to referable diabetic retinopathy (referable background or proliferative retinopathy) or referable maculopathy. The study included 155,114 individuals with no referable diabetic retinopathy or maculopathy at their first DRS examination and with one or more further DRS examinations. There were 11,275 incident cases of referable diabetic eye disease (9,204 referable maculopathy, 2,071 referable background or proliferative retinopathy). The observed transitions to referable background or proliferative retinopathy were lower for people with no visible retinopathy vs mild background retinopathy at their prior examination (respectively, 1.2% vs 8.1% for type 1 diabetes and 0.6% vs 5.1% for type 2 diabetes). The lowest probability for transitioning to referable background or proliferative retinopathy was among people with two consecutive screens showing no visible retinopathy, where the probability was <0.3% for type 1 and <0.2% for type 2 diabetes at 2 years. Transition rates to referable diabetic eye disease were lowest among people with type 2 diabetes and two consecutive screens showing no visible retinopathy. If such people had been offered two yearly screening the DRS service would have needed to screen 40% fewer people in 2009.

Cost-effectiveness of coronary artery disease screening in asymptomatic patients with type 2 diabetes and other atherogenic risk factors in Japan: factors influencing on international application of evidence-based guidelines.

PubMed

Hayashino, Yasuaki; Shimbo, Takuro; Tsujii, Satoru; Ishii, Hitoshi; Kondo, Hirokazu; Nakamura, Tsukasa; Nagata-Kobayashi, Shizuko; Fukui, Tsuguya

2007-05-16

Screening for coronary artery disease (CAD) in asymptomatic diabetic patients with atherogenic risk factors is recommended by the American College of Cardiology/American Diabetes Association. It is not clear whether these guidelines apply to the Japanese population with a different epidemiology of CAD. This study evaluates the applicability of the U.S. guidelines to Japan, taking account of cost-effectiveness. A cost-effectiveness analysis using a Markov model was performed to measure the clinical benefit and cost of CAD screening in asymptomatic patients with diabetes and additional atherogenic risk factors. We evaluated cohorts of patients stratified by age, gender, and atherogenic risks. The incremental cost-effectiveness of not screening, exercise electrocardiography, exercise echocardiography, and exercise single-photon emission-tomography (SPECT) was calculated. The data used were obtained from the literature. Outcomes are expressed as US dollars per quality-adjusted life year (QALY). Compared with not screening, the incremental cost-effectiveness ratio (ICER) of exercise electrocardiography was $31,400/QALY for 60-year-old asymptomatic diabetic men, and 46,600 for 65-year-old women with hypertension and smoking. The ICER of exercise echocardiography was $31,500/QALY and of SPECT was $326,000/QALY, compared with the next dominant strategy. Sensitivity analyses found that these results varied according to age, gender, the combination of additional atherogenic risk factors, and the frequency of screening. From a societal perspective the U.S. guidelines on screening for CAD in high risk diabetic patients are applicable to the Japanese population. However, the population subjected to screening should be carefully selected to obtain greatest benefit from screening.

Home-based screening for biliary atresia using infant stool colour cards: a large-scale prospective cohort study and cost-effectiveness analysis.

PubMed

Schreiber, Richard A; Masucci, Lisa; Kaczorowski, Janusz; Collet, J P; Lutley, Pamela; Espinosa, Victor; Bryan, Stirling

2014-09-01

Biliary atresia (BA), a leading cause of paediatric liver failure and liver transplantation, manifests by three weeks of life as jaundice with acholic stools. Poor outcomes due to delayed diagnosis remain a problem worldwide. We evaluated and assessed the cost-effectiveness of methods of introducing a BA Infant Stool Colour Card (ISCC) screening programme in Canada. A prospective study at BC Women's Hospital recruited consecutive healthy newborns through six incrementally more intensive screening approaches. Under the baseline "passive" strategy, families received ISCCs at maternity, with instructions to monitor infant stool colour daily and return the ISCC by mail at age 30 days. Additional strategies were: ISCC mailed to family physician; reminder letters or telephone calls to families or physicians. Random telephone surveys of ISCC non-returners assessed total card utilization. Primary outcome was ISCC utilization rate expressed as a composite outcome of the ISCC return rate and non-returned ISCC use. Markov modelling was used to predict incremental costs and life years gained from screening (passive and reminder), compared with no screening, over a 10-year time horizon. 6,187 families were enrolled. Card utilization rates in the passive screening strategy were estimated at 60-94%. For a Canadian population, the increase in cost for passive screening, compared with no screening, is $213,584 and the gain in life years is 9.7 ($22,000 per life-year gained). A BA ISCC screening programme targeting families of newborns is feasible in Canada. Passive distribution of ISCC at maternity is potentially effective and highly cost-effective. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Evaluation of Markov-Decision Model for Instructional Sequence Optimization. Semi-Annual Technical Report for the period 1 July-31 December 1975. Technical Report No. 76.

ERIC Educational Resources Information Center

Wollmer, Richard D.; Bond, Nicholas A.

Two computer-assisted instruction programs were written in electronics and trigonometry to test the Wollmer Markov Model for optimizing hierarchial learning; calibration samples totalling 110 students completed these programs. Since the model postulated that transfer effects would be a function of the amount of practice, half of the students were…

Modeling Dyadic Processes Using Hidden Markov Models: A Time Series Approach to Mother-Infant Interactions during Infant Immunization

ERIC Educational Resources Information Center

Stifter, Cynthia A.; Rovine, Michael

2015-01-01

The focus of the present longitudinal study, to examine mother-infant interaction during the administration of immunizations at 2 and 6?months of age, used hidden Markov modelling, a time series approach that produces latent states to describe how mothers and infants work together to bring the infant to a soothed state. Results revealed a…

Intelligent classifier for dynamic fault patterns based on hidden Markov model

NASA Astrophysics Data System (ADS)

Xu, Bo; Feng, Yuguang; Yu, Jinsong

2006-11-01

It's difficult to build precise mathematical models for complex engineering systems because of the complexity of the structure and dynamics characteristics. Intelligent fault diagnosis introduces artificial intelligence and works in a different way without building the analytical mathematical model of a diagnostic object, so it's a practical approach to solve diagnostic problems of complex systems. This paper presents an intelligent fault diagnosis method, an integrated fault-pattern classifier based on Hidden Markov Model (HMM). This classifier consists of dynamic time warping (DTW) algorithm, self-organizing feature mapping (SOFM) network and Hidden Markov Model. First, after dynamic observation vector in measuring space is processed by DTW, the error vector including the fault feature of being tested system is obtained. Then a SOFM network is used as a feature extractor and vector quantization processor. Finally, fault diagnosis is realized by fault patterns classifying with the Hidden Markov Model classifier. The importing of dynamic time warping solves the problem of feature extracting from dynamic process vectors of complex system such as aeroengine, and makes it come true to diagnose complex system by utilizing dynamic process information. Simulating experiments show that the diagnosis model is easy to extend, and the fault pattern classifier is efficient and is convenient to the detecting and diagnosing of new faults.

Present and future of cervical cancer prevention in Spain: a cost-effectiveness analysis.

PubMed

Georgalis, Leonidas; de Sanjosé, Silvia; Esnaola, Mikel; Bosch, F Xavier; Diaz, Mireia

2016-09-01

Human papillomavirus (HPV) vaccination within a nonorganized setting creates a poor cost-effectiveness scenario. However, framed within an organized screening including primary HPV DNA testing with lengthening intervals may provide the best health value for invested money. To compare the effectiveness and cost-effectiveness of different cervical cancer (CC) prevention strategies, including current status and new proposed screening practices, to inform health decision-makers in Spain, a Markov model was developed to simulate the natural history of HPV and CC. Outcomes included cases averted, life expectancy, reduction in the lifetime risk of CC, life years saved, quality-adjusted life years (QALYs), net health benefits, lifetime costs, and incremental cost-effectiveness ratios. The willingness-to-pay threshold is defined at 20 000&OV0556;/QALY. Both costs and health outcomes were discounted at an annual rate of 3%. A strategy of 5-year organized HPV testing has similar effectiveness, but higher efficiency than 3-year cytology. Screening alone and vaccination combined with cytology are dominated by vaccination followed by 5-year HPV testing with cytology triage (12 214&OV0556;/QALY). The optimal age for both ending screening and switching age from cytology to HPV testing in older women is 5 years later for unvaccinated than for vaccinated women. Net health benefits decrease faster with diminishing vaccination coverage than screening coverage. Primary HPV DNA testing is more effective and cost-effective than current cytological screening. Vaccination uptake improvements and a gradual change toward an organized screening practice are critical components for achieving higher effectiveness and efficiency in the prevention of CC in Spain.

Modular techniques for dynamic fault-tree analysis

NASA Technical Reports Server (NTRS)

Patterson-Hine, F. A.; Dugan, Joanne B.

1992-01-01

It is noted that current approaches used to assess the dependability of complex systems such as Space Station Freedom and the Air Traffic Control System are incapable of handling the size and complexity of these highly integrated designs. A novel technique for modeling such systems which is built upon current techniques in Markov theory and combinatorial analysis is described. It enables the development of a hierarchical representation of system behavior which is more flexible than either technique alone. A solution strategy which is based on an object-oriented approach to model representation and evaluation is discussed. The technique is virtually transparent to the user since the fault tree models can be built graphically and the objects defined automatically. The tree modularization procedure allows the two model types, Markov and combinatoric, to coexist and does not require that the entire fault tree be translated to a Markov chain for evaluation. This effectively reduces the size of the Markov chain required and enables solutions with less truncation, making analysis of longer mission times possible. Using the fault-tolerant parallel processor as an example, a model is built and solved for a specific mission scenario and the solution approach is illustrated in detail.

Newborn screening by tandem mass spectrometry for glutaric aciduria type 1: a cost-effectiveness analysis

PubMed Central

2013-01-01

Background Glutaric aciduria type I (GA-I) is a rare metabolic disorder caused by inherited deficiency of glutaryl-CoA dehydrogenase. Despite high prognostic relevance of early diagnosis and start of metabolic treatment as well as an additional cost saving potential later in life, only a limited number of countries recommend newborn screening for GA-I. So far only limited data is available enabling health care decision makers to evaluate whether investing into GA-I screening represents value for money. The aim of our study was therefore to assess the cost-effectiveness of newborn screening for GA-I by tandem mass spectrometry (MS/MS) compared to a scenario where GA-I is not included in the MS/MS screening panel. Methods We assessed the cost-effectiveness of newborn screening for GA-I against the alternative of not including GA-I in MS/MS screening. A Markov model was developed simulating the clinical course of screened and unscreened newborns within different time horizons of 20 and 70 years. Monte Carlo simulation based probabilistic sensitivity analysis was used to determine the probability of GA-I screening representing a cost-effective therapeutic strategy. Results Within a 20 year time horizon, GA-I screening averts approximately 3.7 DALYs (95% CI 2.9 – 4.5) and about one life year is gained (95% CI 0.7 – 1.4) per 100,000 neonates screened initially . Moreover, the screening programme saves a total of around 30,682 Euro (95% CI 14,343 to 49,176 Euro) per 100,000 screened neonates over a 20 year time horizon. Conclusion Within the limitations of the present study, extending pre-existing MS/MS newborn screening programmes by GA-I represents a highly cost-effective diagnostic strategy when assessed under conditions comparable to the German health care system. PMID:24135440

Markov-switching multifractal models as another class of random-energy-like models in one-dimensional space

NASA Astrophysics Data System (ADS)

Saakian, David B.

2012-03-01

We map the Markov-switching multifractal model (MSM) onto the random energy model (REM). The MSM is, like the REM, an exactly solvable model in one-dimensional space with nontrivial correlation functions. According to our results, four different statistical physics phases are possible in random walks with multifractal behavior. We also introduce the continuous branching version of the model, calculate the moments, and prove multiscaling behavior. Different phases have different multiscaling properties.

Multivariate longitudinal data analysis with mixed effects hidden Markov models.

PubMed

Raffa, Jesse D; Dubin, Joel A

2015-09-01

Multiple longitudinal responses are often collected as a means to capture relevant features of the true outcome of interest, which is often hidden and not directly measurable. We outline an approach which models these multivariate longitudinal responses as generated from a hidden disease process. We propose a class of models which uses a hidden Markov model with separate but correlated random effects between multiple longitudinal responses. This approach was motivated by a smoking cessation clinical trial, where a bivariate longitudinal response involving both a continuous and a binomial response was collected for each participant to monitor smoking behavior. A Bayesian method using Markov chain Monte Carlo is used. Comparison of separate univariate response models to the bivariate response models was undertaken. Our methods are demonstrated on the smoking cessation clinical trial dataset, and properties of our approach are examined through extensive simulation studies. © 2015, The International Biometric Society.

Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo

PubMed Central

Golightly, Andrew; Wilkinson, Darren J.

2011-01-01

Computational systems biology is concerned with the development of detailed mechanistic models of biological processes. Such models are often stochastic and analytically intractable, containing uncertain parameters that must be estimated from time course data. In this article, we consider the task of inferring the parameters of a stochastic kinetic model defined as a Markov (jump) process. Inference for the parameters of complex nonlinear multivariate stochastic process models is a challenging problem, but we find here that algorithms based on particle Markov chain Monte Carlo turn out to be a very effective computationally intensive approach to the problem. Approximations to the inferential model based on stochastic differential equations (SDEs) are considered, as well as improvements to the inference scheme that exploit the SDE structure. We apply the methodology to a Lotka–Volterra system and a prokaryotic auto-regulatory network. PMID:23226583

Metagenomic Analysis of Upwelling-Affected Brazilian Coastal Seawater Reveals Sequence Domains of Type I PKS and Modular NRPS

PubMed Central

Cuadrat, Rafael R. C.; Cury, Juliano C.; Dávila, Alberto M. R.

2015-01-01

Marine environments harbor a wide range of microorganisms from the three domains of life. These microorganisms have great potential to enable discovery of new enzymes and bioactive compounds for industrial use. However, only ~1% of microorganisms from the environment can currently be identified through cultured isolates, limiting the discovery of new compounds. To overcome this limitation, a metagenomics approach has been widely adopted for biodiversity studies on samples from marine environments. In this study, we screened metagenomes in order to estimate the potential for new natural compound synthesis mediated by diversity in the Polyketide Synthase (PKS) and Nonribosomal Peptide Synthetase (NRPS) genes. The samples were collected from the Praia dos Anjos (Angel’s Beach) surface water—Arraial do Cabo (Rio de Janeiro state, Brazil), an environment affected by upwelling. In order to evaluate the potential for screening natural products in Arraial do Cabo samples, we used KS (keto-synthase) and C (condensation) domains (from PKS and NRPS, respectively) to build Hidden Markov Models (HMM) models. From both samples, a total of 84 KS and 46 C novel domain sequences were obtained, showing the potential of this environment for the discovery of new genes of biotechnological interest. These domains were classified by phylogenetic analysis and this was the first study conducted to screen PKS and NRPS genes in an upwelling affected sample PMID:26633360

Incorporating interaction networks into the determination of functionally related hit genes in genomic experiments with Markov random fields

PubMed Central

Robinson, Sean; Nevalainen, Jaakko; Pinna, Guillaume; Campalans, Anna; Radicella, J. Pablo; Guyon, Laurent

2017-01-01

Abstract Motivation: Incorporating gene interaction data into the identification of ‘hit’ genes in genomic experiments is a well-established approach leveraging the ‘guilt by association’ assumption to obtain a network based hit list of functionally related genes. We aim to develop a method to allow for multivariate gene scores and multiple hit labels in order to extend the analysis of genomic screening data within such an approach. Results: We propose a Markov random field-based method to achieve our aim and show that the particular advantages of our method compared with those currently used lead to new insights in previously analysed data as well as for our own motivating data. Our method additionally achieves the best performance in an independent simulation experiment. The real data applications we consider comprise of a survival analysis and differential expression experiment and a cell-based RNA interference functional screen. Availability and implementation: We provide all of the data and code related to the results in the paper. Contact: sean.j.robinson@utu.fi or laurent.guyon@cea.fr Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28881978

Tracking Problem Solving by Multivariate Pattern Analysis and Hidden Markov Model Algorithms

ERIC Educational Resources Information Center

Anderson, John R.

2012-01-01

Multivariate pattern analysis can be combined with Hidden Markov Model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first "mind reading" application…

A Markov Chain Monte Carlo Approach to Confirmatory Item Factor Analysis

ERIC Educational Resources Information Center

Edwards, Michael C.

2010-01-01

Item factor analysis has a rich tradition in both the structural equation modeling and item response theory frameworks. The goal of this paper is to demonstrate a novel combination of various Markov chain Monte Carlo (MCMC) estimation routines to estimate parameters of a wide variety of confirmatory item factor analysis models. Further, I show…

Markov Chain Monte Carlo Estimation of Item Parameters for the Generalized Graded Unfolding Model

ERIC Educational Resources Information Center

de la Torre, Jimmy; Stark, Stephen; Chernyshenko, Oleksandr S.

2006-01-01

The authors present a Markov Chain Monte Carlo (MCMC) parameter estimation procedure for the generalized graded unfolding model (GGUM) and compare it to the marginal maximum likelihood (MML) approach implemented in the GGUM2000 computer program, using simulated and real personality data. In the simulation study, test length, number of response…

Generalization of Faustmann's Formula for Stochastic Forest Growth and Prices with Markov Decision Process Models

Treesearch

Joseph Buongiorno

2001-01-01

Faustmann's formula gives the land value, or the forest value of land with trees, under deterministic assumptions regarding future stand growth and prices, over an infinite horizon. Markov decision process (MDP) models generalize Faustmann's approach by recognizing that future stand states and prices are known only as probabilistic distributions. The...

The distribution of genome shared identical by descent for a pair of full sibs by means of the continuous time Markov chain

NASA Astrophysics Data System (ADS)

Julie, Hongki; Pasaribu, Udjianna S.; Pancoro, Adi

2015-12-01

This paper will allow Markov Chain's application in genome shared identical by descent by two individual at full sibs model. The full sibs model was a continuous time Markov Chain with three state. In the full sibs model, we look for the cumulative distribution function of the number of sub segment which have 2 IBD haplotypes from a segment of the chromosome which the length is t Morgan and the cumulative distribution function of the number of sub segment which have at least 1 IBD haplotypes from a segment of the chromosome which the length is t Morgan. This cumulative distribution function will be developed by the moment generating function.

Monte Carlo estimation of total variation distance of Markov chains on large spaces, with application to phylogenetics.

PubMed

Herbei, Radu; Kubatko, Laura

2013-03-26

Markov chains are widely used for modeling in many areas of molecular biology and genetics. As the complexity of such models advances, it becomes increasingly important to assess the rate at which a Markov chain converges to its stationary distribution in order to carry out accurate inference. A common measure of convergence to the stationary distribution is the total variation distance, but this measure can be difficult to compute when the state space of the chain is large. We propose a Monte Carlo method to estimate the total variation distance that can be applied in this situation, and we demonstrate how the method can be efficiently implemented by taking advantage of GPU computing techniques. We apply the method to two Markov chains on the space of phylogenetic trees, and discuss the implications of our findings for the development of algorithms for phylogenetic inference.

A novel framework to simulating non-stationary, non-linear, non-Normal hydrological time series using Markov Switching Autoregressive Models

NASA Astrophysics Data System (ADS)

Birkel, C.; Paroli, R.; Spezia, L.; Tetzlaff, D.; Soulsby, C.

2012-12-01

In this paper we present a novel model framework using the class of Markov Switching Autoregressive Models (MSARMs) to examine catchments as complex stochastic systems that exhibit non-stationary, non-linear and non-Normal rainfall-runoff and solute dynamics. Hereby, MSARMs are pairs of stochastic processes, one observed and one unobserved, or hidden. We model the unobserved process as a finite state Markov chain and assume that the observed process, given the hidden Markov chain, is conditionally autoregressive, which means that the current observation depends on its recent past (system memory). The model is fully embedded in a Bayesian analysis based on Markov Chain Monte Carlo (MCMC) algorithms for model selection and uncertainty assessment. Hereby, the autoregressive order and the dimension of the hidden Markov chain state-space are essentially self-selected. The hidden states of the Markov chain represent unobserved levels of variability in the observed process that may result from complex interactions of hydroclimatic variability on the one hand and catchment characteristics affecting water and solute storage on the other. To deal with non-stationarity, additional meteorological and hydrological time series along with a periodic component can be included in the MSARMs as covariates. This extension allows identification of potential underlying drivers of temporal rainfall-runoff and solute dynamics. We applied the MSAR model framework to streamflow and conservative tracer (deuterium and oxygen-18) time series from an intensively monitored 2.3 km2 experimental catchment in eastern Scotland. Statistical time series analysis, in the form of MSARMs, suggested that the streamflow and isotope tracer time series are not controlled by simple linear rules. MSARMs showed that the dependence of current observations on past inputs observed by transport models often in form of the long-tailing of travel time and residence time distributions can be efficiently explained by non-stationarity either of the system input (climatic variability) and/or the complexity of catchment storage characteristics. The statistical model is also capable of reproducing short (event) and longer-term (inter-event) and wet and dry dynamical "hydrological states". These reflect the non-linear transport mechanisms of flow pathways induced by transient climatic and hydrological variables and modified by catchment characteristics. We conclude that MSARMs are a powerful tool to analyze the temporal dynamics of hydrological data, allowing for explicit integration of non-stationary, non-linear and non-Normal characteristics.

A systematic review of Markov models evaluating multicomponent disease management programs in diabetes.

PubMed

Kirsch, Florian

2015-01-01

Diabetes is the most expensive chronic disease; therefore, disease management programs (DMPs) were introduced. The aim of this review is to determine whether Markov models are adequate to evaluate the cost-effectiveness of complex interventions such as DMPs. Additionally, the quality of the models was evaluated using Philips and Caro quality appraisals. The five reviewed models incorporated the DMP into the model differently: two models integrated effectiveness rates derived from one clinical trial/meta-analysis and three models combined interventions from different sources into a DMP. The results range from cost savings and a QALY gain to costs of US$85,087 per QALY. The Spearman's rank coefficient assesses no correlation between the quality appraisals. With restrictions to the data selection process, Markov models are adequate to determine the cost-effectiveness of DMPs; however, to allow prioritization of medical services, more flexibility in the models is necessary to enable the evaluation of single additional interventions.

Estimation in a semi-Markov transformation model

PubMed Central

Dabrowska, Dorota M.

2012-01-01

Multi-state models provide a common tool for analysis of longitudinal failure time data. In biomedical applications, models of this kind are often used to describe evolution of a disease and assume that patient may move among a finite number of states representing different phases in the disease progression. Several authors developed extensions of the proportional hazard model for analysis of multi-state models in the presence of covariates. In this paper, we consider a general class of censored semi-Markov and modulated renewal processes and propose the use of transformation models for their analysis. Special cases include modulated renewal processes with interarrival times specified using transformation models, and semi-Markov processes with with one-step transition probabilities defined using copula-transformation models. We discuss estimation of finite and infinite dimensional parameters of the model, and develop an extension of the Gaussian multiplier method for setting confidence bands for transition probabilities. A transplant outcome data set from the Center for International Blood and Marrow Transplant Research is used for illustrative purposes. PMID:22740583

Budget Impact Analysis of Against Colorectal Cancer In Our Neighborhoods (ACCION): A Successful Community-Based Colorectal Cancer Screening Program for a Medically Underserved Minority Population.

PubMed

Kim, Bumyang; Lairson, David R; Chung, Tong Han; Kim, Junghyun; Shokar, Navkiran K

2017-06-01

Given the uncertain cost of delivering community-based cancer screening programs, we developed a Markov simulation model to project the budget impact of implementing a comprehensive colorectal cancer (CRC) prevention program compared with the status quo. The study modeled the impacts on the costs of clinical services, materials, and staff expenditures for recruitment, education, fecal immunochemical testing (FIT), colonoscopy, follow-up, navigation, and initial treatment. We used data from the Against Colorectal Cancer In Our Neighborhoods comprehensive CRC prevention program implemented in El Paso, Texas, since 2012. We projected the 3-year financial consequences of the presence and absence of the CRC prevention program for a hypothetical population cohort of 10,000 Hispanic medically underserved individuals. The intervention cohort experienced a 23.4% higher test completion rate for CRC prevention, 8 additional CRC diagnoses, and 84 adenomas. The incremental 3-year cost was $1.74 million compared with the status quo. The program cost per person was $261 compared with $86 for the status quo. The costs were sensitive to the proportion of high-risk participants and the frequency of colonoscopy screening and diagnostic procedures. The budget impact mainly derived from colonoscopy-related costs incurred for the high-risk group. The effectiveness of FIT to detect CRC was critically dependent on follow-up after positive FIT. Community cancer prevention programs need reliable estimates of the cost of CRC screening promotion and the added budget impact of screening with colonoscopy. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

[Cost-effectiveness of multiple screening modalities on breast cancer in Chinese women from Shanghai].

PubMed

Wu, F; Mo, M; Qin, X X; Fang, H; Zhao, G M; Liu, G Y; Chen, Y Y; Cao, Z G; Yan, Y J; Lyu, L L; Xu, W H; Shao, Z M

2017-12-10

Objective: To determine the most cost-effective modality for breast cancer screening in women living in Shanghai. Methods: A Markov model for breast cancer was redeveloped based on true effect which was derived from a project for detection of women at high risk of breast cancer and an organized breast cancer screening program conducted simultaneously in Minhang district, Shanghai, during 2008 to 2012. Parameters of the model were derived from literatures. General principles related to cost-effectiveness analysis were used to compare the costs and effects of 12 different screening modalities in a simulated cohort involving 100 000 women aged 45 years. Incremental cost-effectiveness ratio (ICER) was used to determine the most cost-effective modality. Sensitivity analysis was conducted to evaluate how these factors affected the estimated cost-effectiveness. Results: The modality of biennial CBE followed by ultrasonic and mammography among those with positive CBE was observed as the most cost-effective one. The costs appeared as 182 526 Yuan RMB per life year gained and 144 386 Yuan RMB per quality adjusted life-year (QALY) saved, which were within the threshold of 2-3 times of local per capita Gross Domestic Product. Results from sensitivity analysis showed that, due to higher incidence rate of breast cancer in Shanghai, the cost per QALY would be 64 836 Yuan RMB lower in Shanghai than the average level in China. Conclusion: Our research findings showed that the biennial CBE program followed by ultrasonic and mammography for those with positive CBE results might serve as the optimal breast cancer screening modality for Chinese women living in Shanghai, and thus be widely promoted in this population elsewhere.

Accuracy and Cost-Effectiveness of Cervical Cancer Screening by High-Risk HPV DNA Testing of Self-Collected Vaginal Samples

PubMed Central

Balasubramanian, Akhila; Kulasingam, Shalini L.; Baer, Atar; Hughes, James P.; Myers, Evan R.; Mao, Constance; Kiviat, Nancy B.; Koutsky, Laura A.

2010-01-01

Objective Estimate the accuracy and cost-effectiveness of cervical cancer screening strategies based on high-risk HPV DNA testing of self-collected vaginal samples. Materials and Methods A subset of 1,665 women (18-50 years of age) participating in a cervical cancer screening study were screened by liquid-based cytology and by high-risk HPV DNA testing of both self-collected vaginal swab samples and clinician-collected cervical samples. Women with positive/abnormal screening test results and a subset of women with negative screening test results were triaged to colposcopy. Based on individual and combined test results, five screening strategies were defined. Estimates of sensitivity and specificity for cervical intraepithelial neoplasia grade 2 or worse were calculated and a Markov model was used to estimate the incremental cost-effectiveness ratios (ICERs) for each strategy. Results Compared to cytology-based screening, high-risk HPV DNA testing of self-collected vaginal samples was more sensitive (68%, 95%CI=58%-78% versus 85%, 95%CI=76%-94%) but less specific (89%, 95%CI=86%-91% versus 73%, 95%CI=67%-79%). A strategy of high-risk HPV DNA testing of self-collected vaginal samples followed by cytology triage of HPV positive women, was comparably sensitive (75%, 95%CI=64%-86%) and specific (88%, 95%CI=85%-92%) to cytology-based screening. In-home self-collection for high-risk HPV DNA detection followed by in-clinic cytology triage had a slightly lower lifetime cost and a slightly higher quality-adjusted life expectancy than did cytology-based screening (ICER of triennial screening compared to no screening was $9,871/QALY and $12,878/QALY, respectively). Conclusions Triennial screening by high-risk HPV DNA testing of in-home, self-collected vaginal samples followed by in-clinic cytology triage was cost-effective. PMID:20592553

Semi-Markov Approach to the Shipping Safety Modelling

NASA Astrophysics Data System (ADS)

Guze, Sambor; Smolarek, Leszek

2012-02-01

In the paper the navigational safety model of a ship on the open area has been studied under conditions of incomplete information. Moreover the structure of semi-Markov processes is used to analyse the stochastic ship safety according to the subjective acceptance of risk by the navigator. In addition, the navigator’s behaviour can be analysed by using the numerical simulation to estimate the probability of collision in the safety model.

Three Dimensional Object Recognition Using a Complex Autoregressive Model

DTIC Science & Technology

1993-12-01

3.4.2 Template Matching Algorithm ...................... 3-16 3.4.3 K-Nearest-Neighbor ( KNN ) Techniques ................. 3-25 3.4.4 Hidden Markov Model...Neighbor ( KNN ) Test Results ...................... 4-13 4.2.1 Single-Look 1-NN Testing .......................... 4-14 4.2.2 Multiple-Look 1-NN Testing...4-15 4.2.3 Discussion of KNN Test Results ...................... 4-15 4.3 Hidden Markov Model (HMM) Test Results

Cost-effectiveness analysis of risk-factor guided and birth-cohort screening for chronic hepatitis C infection in the United States.

PubMed

Liu, Shan; Cipriano, Lauren E; Holodniy, Mark; Goldhaber-Fiebert, Jeremy D

2013-01-01

No consensus exists on screening to detect the estimated 2 million Americans unaware of their chronic hepatitis C infections. Advisory groups differ, recommending birth-cohort screening for baby boomers, screening only high-risk individuals, or no screening. We assessed one-time risk assessment and screening to identify previously undiagnosed 40-74 year-olds given newly available hepatitis C treatments. A Markov model evaluated alternative risk-factor guided and birth-cohort screening and treatment strategies. Risk factors included drug use history, blood transfusion before 1992, and multiple sexual partners. Analyses of the National Health and Nutrition Examination Survey provided sex-, race-, age-, and risk-factor-specific hepatitis C prevalence and mortality rates. Nine strategies combined screening (no screening, risk-factor guided screening, or birth-cohort screening) and treatment (standard therapy-peginterferon alfa and ribavirin, Interleukin-28B-guided (IL28B) triple-therapy-standard therapy plus a protease inhibitor, or universal triple therapy). Response-guided treatment depended on HCV genotype. Outcomes include discounted lifetime costs (2010 dollars) and quality adjusted life-years (QALYs). Compared to no screening, risk-factor guided and birth-cohort screening for 50 year-olds gained 0.7 to 3.5 quality adjusted life-days and cost $168 to $568 per person. Birth-cohort screening provided more benefit per dollar than risk-factor guided screening and cost $65,749 per QALY if followed by universal triple therapy compared to screening followed by IL28B-guided triple therapy. If only 10% of screen-detected, eligible patients initiate treatment at each opportunity, birth-cohort screening with universal triple therapy costs $241,100 per QALY. Assuming treatment with triple therapy, screening all individuals aged 40-64 years costs less than $100,000 per QALY. The cost-effectiveness of one-time birth-cohort hepatitis C screening for 40-64 year olds is comparable to other screening programs, provided that the healthcare system has sufficient capacity to deliver prompt treatment and appropriate follow-on care to many newly screen-detected individuals.

Chest Computed Tomographic Image Screening for Cystic Lung Diseases in Patients with Spontaneous Pneumothorax Is Cost Effective

PubMed Central

Langenderfer, Dale; McCormack, Francis X.; Schauer, Daniel P.; Eckman, Mark H.

2017-01-01

Rationale: Patients without a known history of lung disease presenting with a spontaneous pneumothorax are generally diagnosed as having primary spontaneous pneumothorax. However, occult diffuse cystic lung diseases such as Birt-Hogg-Dubé syndrome (BHD), lymphangioleiomyomatosis (LAM), and pulmonary Langerhans cell histiocytosis (PLCH) can also first present with a spontaneous pneumothorax, and their early identification by high-resolution computed tomographic (HRCT) chest imaging has implications for subsequent management. Objectives: The objective of our study was to evaluate the cost-effectiveness of HRCT chest imaging to facilitate early diagnosis of LAM, BHD, and PLCH. Methods: We constructed a Markov state-transition model to assess the cost-effectiveness of screening HRCT to facilitate early diagnosis of diffuse cystic lung diseases in patients presenting with an apparent primary spontaneous pneumothorax. Baseline data for prevalence of BHD, LAM, and PLCH and rates of recurrent pneumothoraces in each of these diseases were derived from the literature. Costs were extracted from 2014 Medicare data. We compared a strategy of HRCT screening followed by pleurodesis in patients with LAM, BHD, or PLCH versus conventional management with no HRCT screening. Measurements and Main Results: In our base case analysis, screening for the presence of BHD, LAM, or PLCH in patients presenting with a spontaneous pneumothorax was cost effective, with a marginal cost-effectiveness ratio of $1,427 per quality-adjusted life-year gained. Sensitivity analysis showed that screening HRCT remained cost effective for diffuse cystic lung diseases prevalence as low as 0.01%. Conclusions: HRCT image screening for BHD, LAM, and PLCH in patients with apparent primary spontaneous pneumothorax is cost effective. Clinicians should consider performing a screening HRCT in patients presenting with apparent primary spontaneous pneumothorax. PMID:27737563

Chest Computed Tomographic Image Screening for Cystic Lung Diseases in Patients with Spontaneous Pneumothorax Is Cost Effective.

PubMed

Gupta, Nishant; Langenderfer, Dale; McCormack, Francis X; Schauer, Daniel P; Eckman, Mark H

2017-01-01

Patients without a known history of lung disease presenting with a spontaneous pneumothorax are generally diagnosed as having primary spontaneous pneumothorax. However, occult diffuse cystic lung diseases such as Birt-Hogg-Dubé syndrome (BHD), lymphangioleiomyomatosis (LAM), and pulmonary Langerhans cell histiocytosis (PLCH) can also first present with a spontaneous pneumothorax, and their early identification by high-resolution computed tomographic (HRCT) chest imaging has implications for subsequent management. The objective of our study was to evaluate the cost-effectiveness of HRCT chest imaging to facilitate early diagnosis of LAM, BHD, and PLCH. We constructed a Markov state-transition model to assess the cost-effectiveness of screening HRCT to facilitate early diagnosis of diffuse cystic lung diseases in patients presenting with an apparent primary spontaneous pneumothorax. Baseline data for prevalence of BHD, LAM, and PLCH and rates of recurrent pneumothoraces in each of these diseases were derived from the literature. Costs were extracted from 2014 Medicare data. We compared a strategy of HRCT screening followed by pleurodesis in patients with LAM, BHD, or PLCH versus conventional management with no HRCT screening. In our base case analysis, screening for the presence of BHD, LAM, or PLCH in patients presenting with a spontaneous pneumothorax was cost effective, with a marginal cost-effectiveness ratio of $1,427 per quality-adjusted life-year gained. Sensitivity analysis showed that screening HRCT remained cost effective for diffuse cystic lung diseases prevalence as low as 0.01%. HRCT image screening for BHD, LAM, and PLCH in patients with apparent primary spontaneous pneumothorax is cost effective. Clinicians should consider performing a screening HRCT in patients presenting with apparent primary spontaneous pneumothorax.

Cost-effectiveness analysis of HLA-B*58: 01 genetic testing before initiation of allopurinol therapy to prevent allopurinol-induced Stevens-Johnson syndrome/toxic epidermal necrolysis in a Malaysian population.

PubMed

Chong, Huey Yi; Lim, Yi Heng; Prawjaeng, Juthamas; Tassaneeyakul, Wichittra; Mohamed, Zahurin; Chaiyakunapruk, Nathorn

2018-02-01

Studies found a strong association between allopurinol-induced Stevens-Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN) and the HLA-B*58:01 allele. HLA-B*58:01 screening-guided therapy may mitigate the risk of allopurinol-induced SJS/TEN. This study aimed to evaluate the cost-effectiveness of HLA-B*58:01 screening before allopurinol therapy initiation compared with the current practice of no screening for Malaysian patients with chronic gout in whom a hypouricemic agent is indicated. This cost-effectiveness analysis adopted a societal perspective with a lifetime horizon. A decision tree model coupled with Markov models were developed to estimate the costs and outcomes, represented by quality-adjusted life years (QALYs) gained, of three treatment strategies: (a) current practice (allopurinol initiation without HLA-B*58:01 screening); (b) HLA-B*58:01 screening before allopurinol initiation; and (c) alternative treatment (probenecid) without HLA-B*58:01 screening. The model was populated with data from literature review, meta-analysis, and published government documents. Cost values were adjusted for the year 2016, with costs and health outcomes discounted at 3% per annum. A series of sensitivity analysis including probabilistic sensitivity analysis were carried out to determine the robustness of the findings. Both HLA-B*58:01 screening and probenecid prescribing were dominated by current practice. Compared with current practice, HLA-B*58:01 screening resulted in 0.252 QALYs loss per patient at an additional cost of USD 322, whereas probenecid prescribing resulted in 1.928 QALYs loss per patient at an additional cost of USD 2203. One SJS/TEN case would be avoided for every 556 patients screened. At the cost-effectiveness threshold of USD 8695 per QALY, the probability of current practice being the best choice is 99.9%, in contrast with 0.1 and 0% in HLA-B*58:01 screening and probenecid prescribing, respectively. This is because of the low incidence of allopurinol-induced SJS/TEN in Malaysia and the lower efficacy of probenecid compared with allopurinol in gout control. This analysis showed that HLA-B*58:01 genetic testing before allopurinol initiation is unlikely to be a cost-effective intervention in Malaysia.

Selecting a mix of prevention strategies against cervical cancer for maximum efficiency with an optimization program.

PubMed

Demarteau, Nadia; Breuer, Thomas; Standaert, Baudouin

2012-04-01

Screening and vaccination against human papillomavirus (HPV) can protect against cervical cancer. Neither alone can provide 100% protection. Consequently it raises the important question about the most efficient combination of screening at specified time intervals and vaccination to prevent cervical cancer. Our objective was to identify the mix of cervical cancer prevention strategies (screening and/or vaccination against HPV) that achieves maximum reduction in cancer cases within a fixed budget. We assessed the optimal mix of strategies for the prevention of cervical cancer using an optimization program. The evaluation used two models. One was a Markov cohort model used as the evaluation model to estimate the costs and outcomes of 52 different prevention strategies. The other was an optimization model in which the results of each prevention strategy of the previous model were entered as input data. The latter model determined the combination of the different prevention options to minimize cervical cancer under budget, screening coverage and vaccination coverage constraints. We applied the model in two countries with different healthcare organizations, epidemiology, screening practices, resource settings and treatment costs: the UK and Brazil. 100,000 women aged 12 years and above across the whole population over a 1-year period at steady state were included. The intervention was papanicolaou (Pap) smear screening programmes and/or vaccination against HPV with the bivalent HPV 16/18 vaccine (Cervarix® [Cervarix is a registered trademark of the GlaxoSmithKline group of companies]). The main outcome measures were optimal distribution of the population between different interventions (screening, vaccination, screening plus vaccination and no screening or vaccination) with the resulting number of cervical cancer and associated costs. In the base-case analysis (= same budget as today), the optimal prevention strategy would be, after introducing vaccination with a coverage rate of 80% in girls aged 12 years and retaining screening coverage at pre-vaccination levels (65% in the UK, 50% in Brazil), to increase the screening interval to 6 years (from 3) in the UK and to 5 years (from 3) in Brazil. This would result in a reduction of cervical cancer by 41% in the UK and by 54% in Brazil from pre-vaccination levels with no budget increase. Sensitivity analysis shows that vaccination alone at 80% coverage with no screening would achieve a cervical cancer reduction rate of 20% in the UK and 43% in Brazil compared with the pre-vaccination situation with a budget reduction of 30% and 14%, respectively. In both countries, the sharp reduction in cervical cancer is seen when the vaccine coverage rate exceeds the maximum screening coverage rate, or when screening coverage rate exceeds the maximum vaccine coverage rate, while maintaining the budget. As with any model, there are limitations to the value of predictions depending upon the assumptions made in each model. Spending the same budget that was used for screening and treatment of cervical cancer in the pre-vaccination era, results of the optimization program show that it would be possible to substantially reduce the number of cases by implementing an optimal combination of HPV vaccination (80% coverage) and screening at pre-vaccination coverage (65% UK, 50% Brazil) while extending the screening interval to every 6 years in the UK and 5 years in Brazil.

A Bayesian model for visual space perception

NASA Technical Reports Server (NTRS)

Curry, R. E.

1972-01-01

A model for visual space perception is proposed that contains desirable features in the theories of Gibson and Brunswik. This model is a Bayesian processor of proximal stimuli which contains three important elements: an internal model of the Markov process describing the knowledge of the distal world, the a priori distribution of the state of the Markov process, and an internal model relating state to proximal stimuli. The universality of the model is discussed and it is compared with signal detection theory models. Experimental results of Kinchla are used as a special case.

Analysis of swallowing sounds using hidden Markov models.

PubMed

Aboofazeli, Mohammad; Moussavi, Zahra

2008-04-01

In recent years, acoustical analysis of the swallowing mechanism has received considerable attention due to its diagnostic potentials. This paper presents a hidden Markov model (HMM) based method for the swallowing sound segmentation and classification. Swallowing sound signals of 15 healthy and 11 dysphagic subjects were studied. The signals were divided into sequences of 25 ms segments each of which were represented by seven features. The sequences of features were modeled by HMMs. Trained HMMs were used for segmentation of the swallowing sounds into three distinct phases, i.e., initial quiet period, initial discrete sounds (IDS) and bolus transit sounds (BTS). Among the seven features, accuracy of segmentation by the HMM based on multi-scale product of wavelet coefficients was higher than that of the other HMMs and the linear prediction coefficient (LPC)-based HMM showed the weakest performance. In addition, HMMs were used for classification of the swallowing sounds of healthy subjects and dysphagic patients. Classification accuracy of different HMM configurations was investigated. When we increased the number of states of the HMMs from 4 to 8, the classification error gradually decreased. In most cases, classification error for N=9 was higher than that of N=8. Among the seven features used, root mean square (RMS) and waveform fractal dimension (WFD) showed the best performance in the HMM-based classification of swallowing sounds. When the sequences of the features of IDS segment were modeled separately, the accuracy reached up to 85.5%. As a second stage classification, a screening algorithm was used which correctly classified all the subjects but one healthy subject when RMS was used as characteristic feature of the swallowing sounds and the number of states was set to N=8.

An Evaluation of a Markov Chain Monte Carlo Method for the Two-Parameter Logistic Model.

ERIC Educational Resources Information Center

Kim, Seock-Ho; Cohen, Allan S.

The accuracy of the Markov Chain Monte Carlo (MCMC) procedure Gibbs sampling was considered for estimation of item parameters of the two-parameter logistic model. Data for the Law School Admission Test (LSAT) Section 6 were analyzed to illustrate the MCMC procedure. In addition, simulated data sets were analyzed using the MCMC, marginal Bayesian…

Causal Latent Markov Model for the Comparison of Multiple Treatments in Observational Longitudinal Studies

ERIC Educational Resources Information Center

Bartolucci, Francesco; Pennoni, Fulvia; Vittadini, Giorgio

2016-01-01

We extend to the longitudinal setting a latent class approach that was recently introduced by Lanza, Coffman, and Xu to estimate the causal effect of a treatment. The proposed approach enables an evaluation of multiple treatment effects on subpopulations of individuals from a dynamic perspective, as it relies on a latent Markov (LM) model that is…

Recovery of Item Parameters in the Nominal Response Model: A Comparison of Marginal Maximum Likelihood Estimation and Markov Chain Monte Carlo Estimation.

ERIC Educational Resources Information Center

Wollack, James A.; Bolt, Daniel M.; Cohen, Allan S.; Lee, Young-Sun

2002-01-01

Compared the quality of item parameter estimates for marginal maximum likelihood (MML) and Markov Chain Monte Carlo (MCMC) with the nominal response model using simulation. The quality of item parameter recovery was nearly identical for MML and MCMC, and both methods tended to produce good estimates. (SLD)

An NCME Instructional Module on Estimating Item Response Theory Models Using Markov Chain Monte Carlo Methods

ERIC Educational Resources Information Center

Kim, Jee-Seon; Bolt, Daniel M.

2007-01-01

The purpose of this ITEMS module is to provide an introduction to Markov chain Monte Carlo (MCMC) estimation for item response models. A brief description of Bayesian inference is followed by an overview of the various facets of MCMC algorithms, including discussion of prior specification, sampling procedures, and methods for evaluating chain…

Multidimensional Latent Markov Models in a Developmental Study of Inhibitory Control and Attentional Flexibility in Early Childhood

ERIC Educational Resources Information Center

Bartolucci, Francesco; Solis-Trapala, Ivonne L.

2010-01-01

We demonstrate the use of a multidimensional extension of the latent Markov model to analyse data from studies with repeated binary responses in developmental psychology. In particular, we consider an experiment based on a battery of tests which was administered to pre-school children, at three time periods, in order to measure their inhibitory…

Hidden Markov models for character recognition.

PubMed

Vlontzos, J A; Kung, S Y

1992-01-01

A hierarchical system for character recognition with hidden Markov model knowledge sources which solve both the context sensitivity problem and the character instantiation problem is presented. The system achieves 97-99% accuracy using a two-level architecture and has been implemented using a systolic array, thus permitting real-time (1 ms per character) multifont and multisize printed character recognition as well as handwriting recognition.

Identification of linear system models and state estimators for controls

NASA Technical Reports Server (NTRS)

Chen, Chung-Wen

1992-01-01

The following paper is presented in viewgraph format and covers topics including: (1) linear state feedback control system; (2) Kalman filter state estimation; (3) relation between residual and stochastic part of output; (4) obtaining Kalman filter gain; (5) state estimation under unknown system model and unknown noises; and (6) relationship between filter Markov parameters and system Markov parameters.

Markov model plus k-word distributions: a synergy that produces novel statistical measures for sequence comparison.

PubMed

Dai, Qi; Yang, Yanchun; Wang, Tianming

2008-10-15

Many proposed statistical measures can efficiently compare biological sequences to further infer their structures, functions and evolutionary information. They are related in spirit because all the ideas for sequence comparison try to use the information on the k-word distributions, Markov model or both. Motivated by adding k-word distributions to Markov model directly, we investigated two novel statistical measures for sequence comparison, called wre.k.r and S2.k.r. The proposed measures were tested by similarity search, evaluation on functionally related regulatory sequences and phylogenetic analysis. This offers the systematic and quantitative experimental assessment of our measures. Moreover, we compared our achievements with these based on alignment or alignment-free. We grouped our experiments into two sets. The first one, performed via ROC (receiver operating curve) analysis, aims at assessing the intrinsic ability of our statistical measures to search for similar sequences from a database and discriminate functionally related regulatory sequences from unrelated sequences. The second one aims at assessing how well our statistical measure is used for phylogenetic analysis. The experimental assessment demonstrates that our similarity measures intending to incorporate k-word distributions into Markov model are more efficient.

Copula-based prediction of economic movements

NASA Astrophysics Data System (ADS)

García, J. E.; González-López, V. A.; Hirsh, I. D.

2016-06-01

In this paper we model the discretized returns of two paired time series BM&FBOVESPA Dividend Index and BM&FBOVESPA Public Utilities Index using multivariate Markov models. The discretization corresponds to three categories, high losses, high profits and the complementary periods of the series. In technical terms, the maximal memory that can be considered for a Markov model, can be derived from the size of the alphabet and dataset. The number of parameters needed to specify a discrete multivariate Markov chain grows exponentially with the order and dimension of the chain. In this case the size of the database is not large enough for a consistent estimation of the model. We apply a strategy to estimate a multivariate process with an order greater than the order achieved using standard procedures. The new strategy consist on obtaining a partition of the state space which is constructed from a combination, of the partitions corresponding to the two marginal processes and the partition corresponding to the multivariate Markov chain. In order to estimate the transition probabilities, all the partitions are linked using a copula. In our application this strategy provides a significant improvement in the movement predictions.

Markov Task Network: A Framework for Service Composition under Uncertainty in Cyber-Physical Systems.

PubMed

Mohammed, Abdul-Wahid; Xu, Yang; Hu, Haixiao; Agyemang, Brighter

2016-09-21

In novel collaborative systems, cooperative entities collaborate services to achieve local and global objectives. With the growing pervasiveness of cyber-physical systems, however, such collaboration is hampered by differences in the operations of the cyber and physical objects, and the need for the dynamic formation of collaborative functionality given high-level system goals has become practical. In this paper, we propose a cross-layer automation and management model for cyber-physical systems. This models the dynamic formation of collaborative services pursuing laid-down system goals as an ontology-oriented hierarchical task network. Ontological intelligence provides the semantic technology of this model, and through semantic reasoning, primitive tasks can be dynamically composed from high-level system goals. In dealing with uncertainty, we further propose a novel bridge between hierarchical task networks and Markov logic networks, called the Markov task network. This leverages the efficient inference algorithms of Markov logic networks to reduce both computational and inferential loads in task decomposition. From the results of our experiments, high-precision service composition under uncertainty can be achieved using this approach.

A Markov Environment-dependent Hurricane Intensity Model and Its Comparison with Multiple Dynamic Models

NASA Astrophysics Data System (ADS)

Jing, R.; Lin, N.; Emanuel, K.; Vecchi, G. A.; Knutson, T. R.

2017-12-01

A Markov environment-dependent hurricane intensity model (MeHiM) is developed to simulate the climatology of hurricane intensity given the surrounding large-scale environment. The model considers three unobserved discrete states representing respectively storm's slow, moderate, and rapid intensification (and deintensification). Each state is associated with a probability distribution of intensity change. The storm's movement from one state to another, regarded as a Markov chain, is described by a transition probability matrix. The initial state is estimated with a Bayesian approach. All three model components (initial intensity, state transition, and intensity change) are dependent on environmental variables including potential intensity, vertical wind shear, midlevel relative humidity, and ocean mixing characteristics. This dependent Markov model of hurricane intensity shows a significant improvement over previous statistical models (e.g., linear, nonlinear, and finite mixture models) in estimating the distributions of 6-h and 24-h intensity change, lifetime maximum intensity, and landfall intensity, etc. Here we compare MeHiM with various dynamical models, including a global climate model [High-Resolution Forecast-Oriented Low Ocean Resolution model (HiFLOR)], a regional hurricane model (Geophysical Fluid Dynamics Laboratory (GFDL) hurricane model), and a simplified hurricane dynamic model [Coupled Hurricane Intensity Prediction System (CHIPS)] and its newly developed fast simulator. The MeHiM developed based on the reanalysis data is applied to estimate the intensity of simulated storms to compare with the dynamical-model predictions under the current climate. The dependences of hurricanes on the environment under current and future projected climates in the various models will also be compared statistically.

Screening strategies for atrial fibrillation: a systematic review and cost-effectiveness analysis.

PubMed

Welton, Nicky J; McAleenan, Alexandra; Thom, Howard Hz; Davies, Philippa; Hollingworth, Will; Higgins, Julian Pt; Okoli, George; Sterne, Jonathan Ac; Feder, Gene; Eaton, Diane; Hingorani, Aroon; Fawsitt, Christopher; Lobban, Trudie; Bryden, Peter; Richards, Alison; Sofat, Reecha

2017-05-01

Atrial fibrillation (AF) is a common cardiac arrhythmia that increases the risk of thromboembolic events. Anticoagulation therapy to prevent AF-related stroke has been shown to be cost-effective. A national screening programme for AF may prevent AF-related events, but would involve a substantial investment of NHS resources. To conduct a systematic review of the diagnostic test accuracy (DTA) of screening tests for AF, update a systematic review of comparative studies evaluating screening strategies for AF, develop an economic model to compare the cost-effectiveness of different screening strategies and review observational studies of AF screening to provide inputs to the model. Systematic review, meta-analysis and cost-effectiveness analysis. Primary care. Adults. Screening strategies, defined by screening test, age at initial and final screens, screening interval and format of screening {systematic opportunistic screening [individuals offered screening if they consult with their general practitioner (GP)] or systematic population screening (when all eligible individuals are invited to screening)}. Sensitivity, specificity and diagnostic odds ratios; the odds ratio of detecting new AF cases compared with no screening; and the mean incremental net benefit compared with no screening. Two reviewers screened the search results, extracted data and assessed the risk of bias. A DTA meta-analysis was perfomed, and a decision tree and Markov model was used to evaluate the cost-effectiveness of the screening strategies. Diagnostic test accuracy depended on the screening test and how it was interpreted. In general, the screening tests identified in our review had high sensitivity (> 0.9). Systematic population and systematic opportunistic screening strategies were found to be similarly effective, with an estimated 170 individuals needed to be screened to detect one additional AF case compared with no screening. Systematic opportunistic screening was more likely to be cost-effective than systematic population screening, as long as the uptake of opportunistic screening observed in randomised controlled trials translates to practice. Modified blood pressure monitors, photoplethysmography or nurse pulse palpation were more likely to be cost-effective than other screening tests. A screening strategy with an initial screening age of 65 years and repeated screens every 5 years until age 80 years was likely to be cost-effective, provided that compliance with treatment does not decline with increasing age. A national screening programme for AF is likely to represent a cost-effective use of resources. Systematic opportunistic screening is more likely to be cost-effective than systematic population screening. Nurse pulse palpation or modified blood pressure monitors would be appropriate screening tests, with confirmation by diagnostic 12-lead electrocardiography interpreted by a trained GP, with referral to a specialist in the case of an unclear diagnosis. Implementation strategies to operationalise uptake of systematic opportunistic screening in primary care should accompany any screening recommendations. Many inputs for the economic model relied on a single trial [the Screening for Atrial Fibrillation in the Elderly (SAFE) study] and DTA results were based on a few studies at high risk of bias/of low applicability. Comparative studies measuring long-term outcomes of screening strategies and DTA studies for new, emerging technologies and to replicate the results for photoplethysmography and GP interpretation of 12-lead electrocardiography in a screening population. This study is registered as PROSPERO CRD42014013739. The National Institute for Health Research Health Technology Assessment programme.

Cost-effectiveness of interferon-γ release assay versus chest X-ray for tuberculosis screening of employees.

PubMed

Kowada, Akiko

2011-12-01

Currently, an annual chest X-ray examination (CXR) for detection of active tuberculosis (TB) in employees aged ≥40 years is recommended in the guidelines of the Japan Industrial Safety and Health Law. Interferon-γ release assays are new alternatives to the tuberculin skin test for detecting Mycobacterium tuberculosis infection, with higher specificity than the tuberculin skin test and without cross-reactivity with the Bacille Calmette-Guérin vaccine. This study aimed to assess the cost-effectiveness of employee TB screening using QuantiFERON-TB Gold In-Tube (QFT) versus CXR. Markov models were constructed. The target population was a hypothetical cohort of immunocompetent 40-year-old individuals, using a societal perspective and a lifetime horizon. All costs and clinical benefits were discounted at a fixed annual rate of 3%. In a base-case analysis, the QFT strategy was the most cost-effective ($US 262.84; 22.87049 quality-adjusted life-years [QALYs]) compared with no screening ($448.38; 22.85452 QALYs) and CXR ($543.50; 22.85453 QALYs) [year 2009 values]. The QFT strategy is currently robust for screening Bacille Calmette-Guérin- vaccinated employees in Japan. There appears to be little role for CXR. These findings may be applicable to other countries in terms of choosing optimal TB screening for employees. Copyright © 2011 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Mosby, Inc. All rights reserved.

Transient Properties of Probability Distribution for a Markov Process with Size-dependent Additive Noise

NASA Astrophysics Data System (ADS)

Yamada, Yuhei; Yamazaki, Yoshihiro

2018-04-01

This study considered a stochastic model for cluster growth in a Markov process with a cluster size dependent additive noise. According to this model, the probability distribution of the cluster size transiently becomes an exponential or a log-normal distribution depending on the initial condition of the growth. In this letter, a master equation is obtained for this model, and derivation of the distributions is discussed.

STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning

PubMed Central

Kappel, David; Nessler, Bernhard; Maass, Wolfgang

2014-01-01

In order to cross a street without being run over, we need to be able to extract very fast hidden causes of dynamically changing multi-modal sensory stimuli, and to predict their future evolution. We show here that a generic cortical microcircuit motif, pyramidal cells with lateral excitation and inhibition, provides the basis for this difficult but all-important information processing capability. This capability emerges in the presence of noise automatically through effects of STDP on connections between pyramidal cells in Winner-Take-All circuits with lateral excitation. In fact, one can show that these motifs endow cortical microcircuits with functional properties of a hidden Markov model, a generic model for solving such tasks through probabilistic inference. Whereas in engineering applications this model is adapted to specific tasks through offline learning, we show here that a major portion of the functionality of hidden Markov models arises already from online applications of STDP, without any supervision or rewards. We demonstrate the emergent computing capabilities of the model through several computer simulations. The full power of hidden Markov model learning can be attained through reward-gated STDP. This is due to the fact that these mechanisms enable a rejection sampling approximation to theoretically optimal learning. We investigate the possible performance gain that can be achieved with this more accurate learning method for an artificial grammar task. PMID:24675787

Hierarchical modeling for reliability analysis using Markov models. B.S./M.S. Thesis - MIT

NASA Technical Reports Server (NTRS)

Fagundo, Arturo

1994-01-01

Markov models represent an extremely attractive tool for the reliability analysis of many systems. However, Markov model state space grows exponentially with the number of components in a given system. Thus, for very large systems Markov modeling techniques alone become intractable in both memory and CPU time. Often a particular subsystem can be found within some larger system where the dependence of the larger system on the subsystem is of a particularly simple form. This simple dependence can be used to decompose such a system into one or more subsystems. A hierarchical technique is presented which can be used to evaluate these subsystems in such a way that their reliabilities can be combined to obtain the reliability for the full system. This hierarchical approach is unique in that it allows the subsystem model to pass multiple aggregate state information to the higher level model, allowing more general systems to be evaluated. Guidelines are developed to assist in the system decomposition. An appropriate method for determining subsystem reliability is also developed. This method gives rise to some interesting numerical issues. Numerical error due to roundoff and integration are discussed at length. Once a decomposition is chosen, the remaining analysis is straightforward but tedious. However, an approach is developed for simplifying the recombination of subsystem reliabilities. Finally, a real world system is used to illustrate the use of this technique in a more practical context.

Markov modeling and reliability analysis of urea synthesis system of a fertilizer plant

NASA Astrophysics Data System (ADS)

Aggarwal, Anil Kr.; Kumar, Sanjeev; Singh, Vikram; Garg, Tarun Kr.

2015-12-01

This paper deals with the Markov modeling and reliability analysis of urea synthesis system of a fertilizer plant. This system was modeled using Markov birth-death process with the assumption that the failure and repair rates of each subsystem follow exponential distribution. The first-order Chapman-Kolmogorov differential equations are developed with the use of mnemonic rule and these equations are solved with Runga-Kutta fourth-order method. The long-run availability, reliability and mean time between failures are computed for various choices of failure and repair rates of subsystems of the system. The findings of the paper are discussed with the plant personnel to adopt and practice suitable maintenance policies/strategies to enhance the performance of the urea synthesis system of the fertilizer plant.

Study of optical and electronic properties of nickel from reflection electron energy loss spectra

NASA Astrophysics Data System (ADS)

Xu, H.; Yang, L. H.; Da, B.; Tóth, J.; Tőkési, K.; Ding, Z. J.

2017-09-01

We use the classical Monte Carlo transport model of electrons moving near the surface and inside solids to reproduce the measured reflection electron energy-loss spectroscopy (REELS) spectra. With the combination of the classical transport model and the Markov chain Monte Carlo (MCMC) sampling of oscillator parameters the so-called reverse Monte Carlo (RMC) method was developed, and used to obtain optical constants of Ni in this work. A systematic study of the electronic and optical properties of Ni has been performed in an energy loss range of 0-200 eV from the measured REELS spectra at primary energies of 1000 eV, 2000 eV and 3000 eV. The reliability of our method was tested by comparing our results with the previous data. Moreover, the accuracy of our optical data has been confirmed by applying oscillator strength-sum rule and perfect-screening-sum rule.

Bayesian clustering of DNA sequences using Markov chains and a stochastic partition model.

PubMed

Jääskinen, Väinö; Parkkinen, Ville; Cheng, Lu; Corander, Jukka

2014-02-01

In many biological applications it is necessary to cluster DNA sequences into groups that represent underlying organismal units, such as named species or genera. In metagenomics this grouping needs typically to be achieved on the basis of relatively short sequences which contain different types of errors, making the use of a statistical modeling approach desirable. Here we introduce a novel method for this purpose by developing a stochastic partition model that clusters Markov chains of a given order. The model is based on a Dirichlet process prior and we use conjugate priors for the Markov chain parameters which enables an analytical expression for comparing the marginal likelihoods of any two partitions. To find a good candidate for the posterior mode in the partition space, we use a hybrid computational approach which combines the EM-algorithm with a greedy search. This is demonstrated to be faster and yield highly accurate results compared to earlier suggested clustering methods for the metagenomics application. Our model is fairly generic and could also be used for clustering of other types of sequence data for which Markov chains provide a reasonable way to compress information, as illustrated by experiments on shotgun sequence type data from an Escherichia coli strain.

Using Partially Observed Markov Decision Processes (POMDPs) to Implement a Response to Intervention (RTI) Framework for Early Reading

ERIC Educational Resources Information Center

Tokac, Umit

2016-01-01

The dissertation explored the efficacy of using a POMDP to select and apply appropriate instruction. POMDPs are a tool for planning: selecting a sequence of actions that will lead to an optimal outcome. RTI is an approach to instruction, where teachers craft individual plans for students based on the results of screening test. The goal is to…

Cost-effectiveness of HIV and syphilis antenatal screening: a modeling study

PubMed Central

Bristow, Claire C.; Larson, Elysia; Anderson, Laura J.; Klausner, Jeffrey D.

2016-01-01

Objectives The World Health Organization called for the elimination of maternal-to-child transmission (MTCT) of HIV and syphilis, a harmonized approach for the improvement of health outcomes for mothers and children. Testing early in pregnancy, treating seropositive pregnant women, and preventing syphilis re-infection can prevent MTCT of HIV and syphilis. We assessed the health and economic outcomes of a dual testing strategy in a simulated cohort of 100,000 antenatal care patients in Malawi. Methods We compared four screening algorithms: (1) HIV rapid test only, (2) dual HIV and syphilis rapid tests, (3) single rapid tests for HIV and syphilis, and (4) HIV rapid and syphilis laboratory tests. We calculated the expected number of adverse pregnancy outcomes, the expected costs, and the expected newborn disability adjusted life years (DALYs) for each screening algorithm. The estimated costs and DALYs for each screening algorithm were assessed from a societal perspective using Markov progression models. Additionally, we conducted a Monte Carlo multi-way sensitivity analysis, allowing for ranges of inputs. Results Our cohort decision model predicted the lowest number of adverse pregnancy outcomes in the dual HIV and syphilis rapid test strategy. Additionally, from the societal perspective, the costs of prevention and care using a dual HIV and syphilis rapid testing strategy was both the least costly ($226.92 per pregnancy) and resulted in the fewest DALYs (116,639) per 100,000 pregnancies. In the Monte Carlo simulation the dual HIV and syphilis algorithm was always cost saving and almost always reduced disability adjusted life years (DALYs) compared to HIV testing alone. Conclusion The results of the cost-effectiveness analysis showed that a dual HIV and syphilis test was cost saving compared to all other screening strategies. Adding dual rapid testing to the existing prevention of mother-to-child HIV transmission programs in Malawi and similar countries is likely to be advantageous. PMID:26920867

Cost-Effectiveness Analysis of a Mobile Ear Screening and Surveillance Service versus an Outreach Screening, Surveillance and Surgical Service for Indigenous Children in Australia

PubMed Central

Nguyen, Kim-Huong; Smith, Anthony C.; Armfield, Nigel R.; Bensink, Mark; Scuffham, Paul A.

2015-01-01

Indigenous Australians experience a high rate of ear disease and hearing loss, yet they have a lower rate of service access and utilisation compared to their non-Indigenous counterparts. Screening, surveillance and timely access to specialist ear, nose and throat (ENT) services are key components in detecting and preventing the recurrence of ear diseases. To address the low access and utilisation rate by Indigenous Australians, a collaborative, community-based mobile telemedicine-enabled screening and surveillance (MTESS) service was trialled in Cherbourg, the third largest Indigenous community in Queensland, Australia. This paper aims to evaluate the cost-effectiveness of the MTESS service using a lifetime Markov model that compares two options: (i) the Deadly Ears Program alone (current practice involving an outreach ENT surgical service and screening program), and (ii) the Deadly Ears Program supplemented with the MTESS service. Data were obtained from the Deadly Ears Program, a feasibility study of the MTESS service and the literature. Incremental cost-utility ratios were calculated from a societal perspective with both costs (in 2013–14 Australian dollars) and quality-adjusted life years (QALYs) discounted at 5% annually. The model showed that compared with the Deadly Ears Program, the probability of an acceptable cost-utility ratio at a willingness-to-pay threshold of $50,000/QALY was 98% for the MTESS service. This cost effectiveness arises from preventing hearing loss in the Indigenous population and the subsequent reduction in associated costs. Deterministic and probability sensitivity analyses indicated that the model was robust to parameter changes. We concluded that the MTESS service is a cost-effective strategy. It presents an opportunity to resolve major issues confronting Australia’s health system such as the inequitable provision and access to quality healthcare for rural and remotes communities, and for Indigenous Australians. Additionally, it may encourage effective health service delivery at a time when the healthcare funding and workforce capacity are limited. PMID:26406592

Cost-Effectiveness Analysis of a Mobile Ear Screening and Surveillance Service versus an Outreach Screening, Surveillance and Surgical Service for Indigenous Children in Australia.

PubMed

Nguyen, Kim-Huong; Smith, Anthony C; Armfield, Nigel R; Bensink, Mark; Scuffham, Paul A

2015-01-01

Indigenous Australians experience a high rate of ear disease and hearing loss, yet they have a lower rate of service access and utilisation compared to their non-Indigenous counterparts. Screening, surveillance and timely access to specialist ear, nose and throat (ENT) services are key components in detecting and preventing the recurrence of ear diseases. To address the low access and utilisation rate by Indigenous Australians, a collaborative, community-based mobile telemedicine-enabled screening and surveillance (MTESS) service was trialled in Cherbourg, the third largest Indigenous community in Queensland, Australia. This paper aims to evaluate the cost-effectiveness of the MTESS service using a lifetime Markov model that compares two options: (i) the Deadly Ears Program alone (current practice involving an outreach ENT surgical service and screening program), and (ii) the Deadly Ears Program supplemented with the MTESS service. Data were obtained from the Deadly Ears Program, a feasibility study of the MTESS service and the literature. Incremental cost-utility ratios were calculated from a societal perspective with both costs (in 2013-14 Australian dollars) and quality-adjusted life years (QALYs) discounted at 5% annually. The model showed that compared with the Deadly Ears Program, the probability of an acceptable cost-utility ratio at a willingness-to-pay threshold of $50,000/QALY was 98% for the MTESS service. This cost effectiveness arises from preventing hearing loss in the Indigenous population and the subsequent reduction in associated costs. Deterministic and probability sensitivity analyses indicated that the model was robust to parameter changes. We concluded that the MTESS service is a cost-effective strategy. It presents an opportunity to resolve major issues confronting Australia's health system such as the inequitable provision and access to quality healthcare for rural and remotes communities, and for Indigenous Australians. Additionally, it may encourage effective health service delivery at a time when the healthcare funding and workforce capacity are limited.

Transition records of stationary Markov chains.

PubMed

Naudts, Jan; Van der Straeten, Erik

2006-10-01

In any Markov chain with finite state space the distribution of transition records always belongs to the exponential family. This observation is used to prove a fluctuation theorem, and to show that the dynamical entropy of a stationary Markov chain is linear in the number of steps. Three applications are discussed. A known result about entropy production is reproduced. A thermodynamic relation is derived for equilibrium systems with Metropolis dynamics. Finally, a link is made with recent results concerning a one-dimensional polymer model.

Numerical research of the optimal control problem in the semi-Markov inventory model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gorshenin, Andrey K.; Belousov, Vasily V.; Shnourkoff, Peter V.

2015-03-10

This paper is devoted to the numerical simulation of stochastic system for inventory management products using controlled semi-Markov process. The results of a special software for the system’s research and finding the optimal control are presented.

Automatic specification of reliability models for fault-tolerant computers

NASA Technical Reports Server (NTRS)

Liceaga, Carlos A.; Siewiorek, Daniel P.

1993-01-01

The calculation of reliability measures using Markov models is required for life-critical processor-memory-switch structures that have standby redundancy or that are subject to transient or intermittent faults or repair. The task of specifying these models is tedious and prone to human error because of the large number of states and transitions required in any reasonable system. Therefore, model specification is a major analysis bottleneck, and model verification is a major validation problem. The general unfamiliarity of computer architects with Markov modeling techniques further increases the necessity of automating the model specification. Automation requires a general system description language (SDL). For practicality, this SDL should also provide a high level of abstraction and be easy to learn and use. The first attempt to define and implement an SDL with those characteristics is presented. A program named Automated Reliability Modeling (ARM) was constructed as a research vehicle. The ARM program uses a graphical interface as its SDL, and it outputs a Markov reliability model specification formulated for direct use by programs that generate and evaluate the model.

Effects of stochastic interest rates in decision making under risk: A Markov decision process model for forest management

Treesearch

Mo Zhou; Joseph Buongiorno

2011-01-01

Most economic studies of forest decision making under risk assume a fixed interest rate. This paper investigated some implications of this stochastic nature of interest rates. Markov decision process (MDP) models, used previously to integrate stochastic stand growth and prices, can be extended to include variable interest rates as well. This method was applied to...

Markov State Models of gene regulatory networks.

PubMed

Chu, Brian K; Tse, Margaret J; Sato, Royce R; Read, Elizabeth L

2017-02-06

Gene regulatory networks with dynamics characterized by multiple stable states underlie cell fate-decisions. Quantitative models that can link molecular-level knowledge of gene regulation to a global understanding of network dynamics have the potential to guide cell-reprogramming strategies. Networks are often modeled by the stochastic Chemical Master Equation, but methods for systematic identification of key properties of the global dynamics are currently lacking. The method identifies the number, phenotypes, and lifetimes of long-lived states for a set of common gene regulatory network models. Application of transition path theory to the constructed Markov State Model decomposes global dynamics into a set of dominant transition paths and associated relative probabilities for stochastic state-switching. In this proof-of-concept study, we found that the Markov State Model provides a general framework for analyzing and visualizing stochastic multistability and state-transitions in gene networks. Our results suggest that this framework-adopted from the field of atomistic Molecular Dynamics-can be a useful tool for quantitative Systems Biology at the network scale.

Mathematical model of the loan portfolio dynamics in the form of Markov chain considering the process of new customers attraction

NASA Astrophysics Data System (ADS)

Bozhalkina, Yana

2017-12-01

Mathematical model of the loan portfolio structure change in the form of Markov chain is explored. This model considers in one scheme both the process of customers attraction, their selection based on the credit score, and loans repayment. The model describes the structure and volume of the loan portfolio dynamics, which allows to make medium-term forecasts of profitability and risk. Within the model corrective actions of bank management in order to increase lending volumes or to reduce the risk are formalized.

ASSIST: User's manual

NASA Technical Reports Server (NTRS)

Johnson, S. C.

1986-01-01

Semi-Markov models can be used to compute the reliability of virtually any fault-tolerant system. However, the process of delineating all of the states and transitions in a model of a complex system can be devastingly tedious and error-prone. The ASSIST program allows the user to describe the semi-Markov model in a high-level language. Instead of specifying the individual states of the model, the user specifies the rules governing the behavior of the system and these are used by ASSIST to automatically generate the model. The ASSIST program is described and illustrated by examples.

Cost-effectiveness analysis of the introduction of a quadrivalent human papillomavirus vaccine in France.

PubMed

Bergeron, Christine; Largeron, Nathalie; McAllister, Ruth; Mathevet, Patrice; Remy, Vanessa

2008-01-01

A vaccine to prevent diseases due to human papillomavirus (HPV) types 6, 11, 16, and 18 is now available in France. The objective of this study was to assess the health and economic impact in France of implementing a quadrivalent HPV vaccine alongside existing screening practices versus screening alone. A Markov model of the natural history of HPV infection incorporating screening and vaccination, was adapted to the French context. A vaccine that would prevent 100 percent of HPV 6, 11, 16, and 18-associated diseases, with lifetime duration and 80 percent coverage, given to girls at age 14 in conjunction with current screening was compared with screening alone. Results were analyzed from both a direct healthcare cost perspective (DCP) and a third-party payer perspective (TPP). Indirect costs such as productivity loss were not taken into account in this analysis. The incremental cost per life-year gained from vaccination was euro12,429 (TPP) and euro20,455 (DCP). The incremental cost per quality-adjusted life-year (QALY) for the introduction of HPV vaccination alongside the French cervical cancer screening program was euro8,408 (TPP) and euro13,809 (DCP). Sensitivity analyses demonstrated that cost-effectiveness was stable, but was most sensitive to the discount rate used for costs and benefits. Considering the commonly accepted threshold of euro50,000 per QALY, these analyses support the fact that adding a quadrivalent HPV vaccine to the current screening program in France is a cost-effective strategy for reducing the burden of cervical cancer, precancerous lesions, and genital warts caused by HPV types 6, 11, 16, and 18.

Using resource modelling to inform decision making and service planning: the case of colorectal cancer screening in Ireland

PubMed Central

2013-01-01

Background Organised colorectal cancer screening is likely to be cost-effective, but cost-effectiveness results alone may not help policy makers to make decisions about programme feasibility or service providers to plan programme delivery. For these purposes, estimates of the impact on the health services of actually introducing screening in the target population would be helpful. However, these types of analyses are rarely reported. As an illustration of such an approach, we estimated annual health service resource requirements and health outcomes over the first decade of a population-based colorectal cancer screening programme in Ireland. Methods A Markov state-transition model of colorectal neoplasia natural history was used. Three core screening scenarios were considered: (a) flexible sigmoidoscopy (FSIG) once at age 60, (b) biennial guaiac-based faecal occult blood tests (gFOBT) at 55–74 years, and (c) biennial faecal immunochemical tests (FIT) at 55–74 years. Three alternative FIT roll-out scenarios were also investigated relating to age-restricted screening (55–64 years) and staggered age-based roll-out across the 55–74 age group. Parameter estimates were derived from literature review, existing screening programmes, and expert opinion. Results were expressed in relation to the 2008 population (4.4 million people, of whom 700,800 were aged 55–74). Results FIT-based screening would deliver the greatest health benefits, averting 164 colorectal cancer cases and 272 deaths in year 10 of the programme. Capacity would be required for 11,095-14,820 diagnostic and surveillance colonoscopies annually, compared to 381–1,053 with FSIG-based, and 967–1,300 with gFOBT-based, screening. With FIT, in year 10, these colonoscopies would result in 62 hospital admissions for abdominal bleeding, 27 bowel perforations and one death. Resource requirements for pathology, diagnostic radiology, radiotherapy and colorectal resection were highest for FIT. Estimates depended on screening uptake. Alternative FIT roll-out scenarios had lower resource requirements. Conclusions While FIT-based screening would quite quickly generate attractive health outcomes, it has heavy resource requirements. These could impact on the feasibility of a programme based on this screening modality. Staggered age-based roll-out would allow time to increase endoscopy capacity to meet programme requirements. Resource modelling of this type complements conventional cost-effectiveness analyses and can help inform policy making and service planning. PMID:23510135

Markov Decision Process Measurement Model.

PubMed

LaMar, Michelle M

2018-03-01

Within-task actions can provide additional information on student competencies but are challenging to model. This paper explores the potential of using a cognitive model for decision making, the Markov decision process, to provide a mapping between within-task actions and latent traits of interest. Psychometric properties of the model are explored, and simulation studies report on parameter recovery within the context of a simple strategy game. The model is then applied to empirical data from an educational game. Estimates from the model are found to correlate more strongly with posttest results than a partial-credit IRT model based on outcome data alone.

Surgical gesture segmentation and recognition.

PubMed

Tao, Lingling; Zappella, Luca; Hager, Gregory D; Vidal, René

2013-01-01

Automatic surgical gesture segmentation and recognition can provide useful feedback for surgical training in robotic surgery. Most prior work in this field relies on the robot's kinematic data. Although recent work [1,2] shows that the robot's video data can be equally effective for surgical gesture recognition, the segmentation of the video into gestures is assumed to be known. In this paper, we propose a framework for joint segmentation and recognition of surgical gestures from kinematic and video data. Unlike prior work that relies on either frame-level kinematic cues, or segment-level kinematic or video cues, our approach exploits both cues by using a combined Markov/semi-Markov conditional random field (MsM-CRF) model. Our experiments show that the proposed model improves over a Markov or semi-Markov CRF when using video data alone, gives results that are comparable to state-of-the-art methods on kinematic data alone, and improves over state-of-the-art methods when combining kinematic and video data.

Trans-dimensional matched-field geoacoustic inversion with hierarchical error models and interacting Markov chains.

PubMed

Dettmer, Jan; Dosso, Stan E

2012-10-01

This paper develops a trans-dimensional approach to matched-field geoacoustic inversion, including interacting Markov chains to improve efficiency and an autoregressive model to account for correlated errors. The trans-dimensional approach and hierarchical seabed model allows inversion without assuming any particular parametrization by relaxing model specification to a range of plausible seabed models (e.g., in this case, the number of sediment layers is an unknown parameter). Data errors are addressed by sampling statistical error-distribution parameters, including correlated errors (covariance), by applying a hierarchical autoregressive error model. The well-known difficulty of low acceptance rates for trans-dimensional jumps is addressed with interacting Markov chains, resulting in a substantial increase in efficiency. The trans-dimensional seabed model and the hierarchical error model relax the degree of prior assumptions required in the inversion, resulting in substantially improved (more realistic) uncertainty estimates and a more automated algorithm. In particular, the approach gives seabed parameter uncertainty estimates that account for uncertainty due to prior model choice (layering and data error statistics). The approach is applied to data measured on a vertical array in the Mediterranean Sea.

ZebraZoom: an automated program for high-throughput behavioral analysis and categorization

PubMed Central

Mirat, Olivier; Sternberg, Jenna R.; Severi, Kristen E.; Wyart, Claire

2013-01-01

The zebrafish larva stands out as an emergent model organism for translational studies involving gene or drug screening thanks to its size, genetics, and permeability. At the larval stage, locomotion occurs in short episodes punctuated by periods of rest. Although phenotyping behavior is a key component of large-scale screens, it has not yet been automated in this model system. We developed ZebraZoom, a program to automatically track larvae and identify maneuvers for many animals performing discrete movements. Our program detects each episodic movement and extracts large-scale statistics on motor patterns to produce a quantification of the locomotor repertoire. We used ZebraZoom to identify motor defects induced by a glycinergic receptor antagonist. The analysis of the blind mutant atoh7 revealed small locomotor defects associated with the mutation. Using multiclass supervised machine learning, ZebraZoom categorized all episodes of movement for each larva into one of three possible maneuvers: slow forward swim, routine turn, and escape. ZebraZoom reached 91% accuracy for categorization of stereotypical maneuvers that four independent experimenters unanimously identified. For all maneuvers in the data set, ZebraZoom agreed with four experimenters in 73.2–82.5% of cases. We modeled the series of maneuvers performed by larvae as Markov chains and observed that larvae often repeated the same maneuvers within a group. When analyzing subsequent maneuvers performed by different larvae, we found that larva–larva interactions occurred as series of escapes. Overall, ZebraZoom reached the level of precision found in manual analysis but accomplished tasks in a high-throughput format necessary for large screens. PMID:23781175

Markov chain decision model for urinary incontinence procedures.

PubMed

Kumar, Sameer; Ghildayal, Nidhi; Ghildayal, Neha

2017-03-13

Purpose Urinary incontinence (UI) is a common chronic health condition, a problem specifically among elderly women that impacts quality of life negatively. However, UI is usually viewed as likely result of old age, and as such is generally not evaluated or even managed appropriately. Many treatments are available to manage incontinence, such as bladder training and numerous surgical procedures such as Burch colposuspension and Sling for UI which have high success rates. The purpose of this paper is to analyze which of these popular surgical procedures for UI is effective. Design/methodology/approach This research employs randomized, prospective studies to obtain robust cost and utility data used in the Markov chain decision model for examining which of these surgical interventions is more effective in treating women with stress UI based on two measures: number of quality adjusted life years (QALY) and cost per QALY. Treeage Pro Healthcare software was employed in Markov decision analysis. Findings Results showed the Sling procedure is a more effective surgical intervention than the Burch. However, if a utility greater than certain utility value, for which both procedures are equally effective, is assigned to persistent incontinence, the Burch procedure is more effective than the Sling procedure. Originality/value This paper demonstrates the efficacy of a Markov chain decision modeling approach to study the comparative effectiveness analysis of available treatments for patients with UI, an important public health issue, widely prevalent among elderly women in developed and developing countries. This research also improves upon other analyses using a Markov chain decision modeling process to analyze various strategies for treating UI.

Overshoot in biological systems modelled by Markov chains: a non-equilibrium dynamic phenomenon.

PubMed

Jia, Chen; Qian, Minping; Jiang, Daquan

2014-08-01

A number of biological systems can be modelled by Markov chains. Recently, there has been an increasing concern about when biological systems modelled by Markov chains will perform a dynamic phenomenon called overshoot. In this study, the authors found that the steady-state behaviour of the system will have a great effect on the occurrence of overshoot. They showed that overshoot in general cannot occur in systems that will finally approach an equilibrium steady state. They further classified overshoot into two types, named as simple overshoot and oscillating overshoot. They showed that except for extreme cases, oscillating overshoot will occur if the system is far from equilibrium. All these results clearly show that overshoot is a non-equilibrium dynamic phenomenon with energy consumption. In addition, the main result in this study is validated with real experimental data.

Cost-effectiveness of breast cancer screening using mammography in Vietnamese women

PubMed Central

2018-01-01

Background The incidence rate of breast cancer is increasing and has become the most common cancer in Vietnamese women while the survival rate is lower than that of developed countries. Early detection to improve breast cancer survival as well as reducing risk factors remains the cornerstone of breast cancer control according to the World Health Organization (WHO). This study aims to evaluate the costs and outcomes of introducing a mammography screening program for Vietnamese women aged 45–64 years, compared to the current situation of no screening. Methods Decision analytical modeling using Markov chain analysis was used to estimate costs and health outcomes over a lifetime horizon. Model inputs were derived from published literature and the results were reported as incremental cost-effectiveness ratios (ICERs) and/or incremental net monetary benefits (INMBs). One-way sensitivity analyses and probabilistic sensitivity analyses were performed to assess parameter uncertainty. Results The ICER per life year gained of the first round of mammography screening was US$3647.06 and US$4405.44 for women aged 50–54 years and 55–59 years, respectively. In probabilistic sensitivity analyses, mammography screening in the 50–54 age group and the 55–59 age group were cost-effective in 100% of cases at a threshold of three times the Vietnamese Gross Domestic Product (GDP) i.e., US$6332.70. However, less than 50% of the cases in the 60–64 age group and 0% of the cases in the 45–49 age group were cost effective at the WHO threshold. The ICERs were sensitive to the discount rate, mammography sensitivity, and transition probability from remission to distant recurrence in stage II for all age groups. Conclusion From the healthcare payer viewpoint, offering the first round of mammography screening to Vietnamese women aged 50–59 years should be considered, with the given threshold of three times the Vietnamese GDP per capita. PMID:29579131

Comparative effectiveness and cost-effectiveness of screening colonoscopy vs. sigmoidoscopy and alternative strategies.

PubMed

Sharaf, Ravi N; Ladabaum, Uri

2013-01-01

Fecal occult blood testing (FOBT) and sigmoidoscopy are proven to decrease colorectal cancer (CRC) incidence and mortality. Sigmoidoscopy's benefit is limited to the distal colon. Observational data are conflicting regarding the degree to which colonoscopy affords protection against proximal CRC. Our aim was to explore the comparative effectiveness and cost-effectiveness of colonoscopy vs. sigmoidoscopy and alternative CRC screening strategies in light of the latest published data. We performed a contemporary cost-utility analysis using a Markov model validated against data from randomized controlled trials of FOBT and sigmoidoscopy. Persons at average CRC risk within the general US population were modeled. Screening strategies included those recommended by the United States (US) Preventive Services Task Force, including colonoscopy every 10 years (COLO), flexible sigmoidoscopy every 5 years (FS), annual fecal occult blood testing, annual fecal immunochemical testing (FIT), and the combination FS/FIT. The main outcome measures were quality-adjusted life-years (QALYs) and costs. In the base case, FIT dominated other strategies. The advantage of FIT over FS and COLO was contingent on rates of uptake and adherence that are well above current US rates. Compared with FIT, FS and COLO both cost <$50,000/QALY gained when FIT per-cycle adherence was <50%. COLO cost $56,800/QALY gained vs. FS in the base case. COLO cost <$100,000/QALY gained vs. FS when COLO yielded a relative risk of proximal CRC of <0.5 vs. no screening. In probabilistic analyses, COLO was cost-effective vs. FS at a willingness-to-pay threshold of $100,000/QALY gained in 84% of iterations. Screening colonoscopy may be cost-effective compared with FIT and sigmoidoscopy, depending on the relative rates of screening uptake and adherence and the protective benefit of colonoscopy in the proximal colon. Colonoscopy's cost-effectiveness compared with sigmoidoscopy is contingent on the ability to deliver ~50% protection against CRC in the proximal colon.

A Systematic Approach to Determining the Identifiability of Multistage Carcinogenesis Models.

PubMed

Brouwer, Andrew F; Meza, Rafael; Eisenberg, Marisa C

2017-07-01

Multistage clonal expansion (MSCE) models of carcinogenesis are continuous-time Markov process models often used to relate cancer incidence to biological mechanism. Identifiability analysis determines what model parameter combinations can, theoretically, be estimated from given data. We use a systematic approach, based on differential algebra methods traditionally used for deterministic ordinary differential equation (ODE) models, to determine identifiable combinations for a generalized subclass of MSCE models with any number of preinitation stages and one clonal expansion. Additionally, we determine the identifiable combinations of the generalized MSCE model with up to four clonal expansion stages, and conjecture the results for any number of clonal expansion stages. The results improve upon previous work in a number of ways and provide a framework to find the identifiable combinations for further variations on the MSCE models. Finally, our approach, which takes advantage of the Kolmogorov backward equations for the probability generating functions of the Markov process, demonstrates that identifiability methods used in engineering and mathematics for systems of ODEs can be applied to continuous-time Markov processes. © 2016 Society for Risk Analysis.

Document Ranking Based upon Markov Chains.

ERIC Educational Resources Information Center

Danilowicz, Czeslaw; Balinski, Jaroslaw

2001-01-01

Considers how the order of documents in information retrieval responses are determined and introduces a method that uses a probabilistic model of a document set where documents are regarded as states of a Markov chain and where transition probabilities are directly proportional to similarities between documents. (Author/LRW)

A method of hidden Markov model optimization for use with geophysical data sets

NASA Technical Reports Server (NTRS)

Granat, R. A.

2003-01-01

Geophysics research has been faced with a growing need for automated techniques with which to process large quantities of data. A successful tool must meet a number of requirements: it should be consistent, require minimal parameter tuning, and produce scientifically meaningful results in reasonable time. We introduce a hidden Markov model (HMM)-based method for analysis of geophysical data sets that attempts to address these issues.

The Embedding Problem for Markov Models of Nucleotide Substitution

PubMed Central

Verbyla, Klara L.; Yap, Von Bing; Pahwa, Anuj; Shao, Yunli; Huttley, Gavin A.

2013-01-01

Continuous-time Markov processes are often used to model the complex natural phenomenon of sequence evolution. To make the process of sequence evolution tractable, simplifying assumptions are often made about the sequence properties and the underlying process. The validity of one such assumption, time-homogeneity, has never been explored. Violations of this assumption can be found by identifying non-embeddability. A process is non-embeddable if it can not be embedded in a continuous time-homogeneous Markov process. In this study, non-embeddability was demonstrated to exist when modelling sequence evolution with Markov models. Evidence of non-embeddability was found primarily at the third codon position, possibly resulting from changes in mutation rate over time. Outgroup edges and those with a deeper time depth were found to have an increased probability of the underlying process being non-embeddable. Overall, low levels of non-embeddability were detected when examining individual edges of triads across a diverse set of alignments. Subsequent phylogenetic reconstruction analyses demonstrated that non-embeddability could impact on the correct prediction of phylogenies, but at extremely low levels. Despite the existence of non-embeddability, there is minimal evidence of violations of the local time homogeneity assumption and consequently the impact is likely to be minor. PMID:23935949

A Hidden Markov Model for Analysis of Frontline Veterinary Data for Emerging Zoonotic Disease Surveillance

PubMed Central

Robertson, Colin; Sawford, Kate; Gunawardana, Walimunige S. N.; Nelson, Trisalyn A.; Nathoo, Farouk; Stephen, Craig

2011-01-01

Surveillance systems tracking health patterns in animals have potential for early warning of infectious disease in humans, yet there are many challenges that remain before this can be realized. Specifically, there remains the challenge of detecting early warning signals for diseases that are not known or are not part of routine surveillance for named diseases. This paper reports on the development of a hidden Markov model for analysis of frontline veterinary sentinel surveillance data from Sri Lanka. Field veterinarians collected data on syndromes and diagnoses using mobile phones. A model for submission patterns accounts for both sentinel-related and disease-related variability. Models for commonly reported cattle diagnoses were estimated separately. Region-specific weekly average prevalence was estimated for each diagnoses and partitioned into normal and abnormal periods. Visualization of state probabilities was used to indicate areas and times of unusual disease prevalence. The analysis suggests that hidden Markov modelling is a useful approach for surveillance datasets from novel populations and/or having little historical baselines. PMID:21949763

Modeling long correlation times using additive binary Markov chains: Applications to wind generation time series.

PubMed

Weber, Juliane; Zachow, Christopher; Witthaut, Dirk

2018-03-01

Wind power generation exhibits a strong temporal variability, which is crucial for system integration in highly renewable power systems. Different methods exist to simulate wind power generation but they often cannot represent the crucial temporal fluctuations properly. We apply the concept of additive binary Markov chains to model a wind generation time series consisting of two states: periods of high and low wind generation. The only input parameter for this model is the empirical autocorrelation function. The two-state model is readily extended to stochastically reproduce the actual generation per period. To evaluate the additive binary Markov chain method, we introduce a coarse model of the electric power system to derive backup and storage needs. We find that the temporal correlations of wind power generation, the backup need as a function of the storage capacity, and the resting time distribution of high and low wind events for different shares of wind generation can be reconstructed.

Modeling long correlation times using additive binary Markov chains: Applications to wind generation time series

NASA Astrophysics Data System (ADS)

Weber, Juliane; Zachow, Christopher; Witthaut, Dirk

2018-03-01

Wind power generation exhibits a strong temporal variability, which is crucial for system integration in highly renewable power systems. Different methods exist to simulate wind power generation but they often cannot represent the crucial temporal fluctuations properly. We apply the concept of additive binary Markov chains to model a wind generation time series consisting of two states: periods of high and low wind generation. The only input parameter for this model is the empirical autocorrelation function. The two-state model is readily extended to stochastically reproduce the actual generation per period. To evaluate the additive binary Markov chain method, we introduce a coarse model of the electric power system to derive backup and storage needs. We find that the temporal correlations of wind power generation, the backup need as a function of the storage capacity, and the resting time distribution of high and low wind events for different shares of wind generation can be reconstructed.

Predicting Urban Medical Services Demand in China: An Improved Grey Markov Chain Model by Taylor Approximation.

PubMed

Duan, Jinli; Jiao, Feng; Zhang, Qishan; Lin, Zhibin

2017-08-06

The sharp increase of the aging population has raised the pressure on the current limited medical resources in China. To better allocate resources, a more accurate prediction on medical service demand is very urgently needed. This study aims to improve the prediction on medical services demand in China. To achieve this aim, the study combines Taylor Approximation into the Grey Markov Chain model, and develops a new model named Taylor-Markov Chain GM (1,1) (T-MCGM (1,1)). The new model has been tested by adopting the historical data, which includes the medical service on treatment of diabetes, heart disease, and cerebrovascular disease from 1997 to 2015 in China. The model provides a predication on medical service demand of these three types of disease up to 2022. The results reveal an enormous growth of urban medical service demand in the future. The findings provide practical implications for the Health Administrative Department to allocate medical resources, and help hospitals to manage investments on medical facilities.

Decentralized control of Markovian decision processes: Existence Sigma-admissable policies

NASA Technical Reports Server (NTRS)

Greenland, A.

1980-01-01

The problem of formulating and analyzing Markov decision models having decentralized information and decision patterns is examined. Included are basic examples as well as the mathematical preliminaries needed to understand Markov decision models and, further, to superimpose decentralized decision structures on them. The notion of a variance admissible policy for the model is introduced and it is proved that there exist (possibly nondeterministic) optional policies from the class of variance admissible policies. Directions for further research are explored.

Simplification of irreversible Markov chains by removal of states with fast leaving rates.

PubMed

Jia, Chen

2016-07-07

In the recent work of Ullah et al. (2012a), the authors developed an effective method to simplify reversible Markov chains by removal of states with low equilibrium occupancies. In this paper, we extend this result to irreversible Markov chains. We show that an irreversible chain can be simplified by removal of states with fast leaving rates. Moreover, we reveal that the irreversibility of the chain will always decrease after model simplification. This suggests that although model simplification can retain almost all the dynamic information of the chain, it will lose some thermodynamic information as a trade-off. Examples from biology are also given to illustrate the main results of this paper. Copyright © 2016 Elsevier Ltd. All rights reserved.

Optimal clinical trial design based on a dichotomous Markov-chain mixed-effect sleep model.

PubMed

Steven Ernest, C; Nyberg, Joakim; Karlsson, Mats O; Hooker, Andrew C

2014-12-01

D-optimal designs for discrete-type responses have been derived using generalized linear mixed models, simulation based methods and analytical approximations for computing the fisher information matrix (FIM) of non-linear mixed effect models with homogeneous probabilities over time. In this work, D-optimal designs using an analytical approximation of the FIM for a dichotomous, non-homogeneous, Markov-chain phase advanced sleep non-linear mixed effect model was investigated. The non-linear mixed effect model consisted of transition probabilities of dichotomous sleep data estimated as logistic functions using piecewise linear functions. Theoretical linear and nonlinear dose effects were added to the transition probabilities to modify the probability of being in either sleep stage. D-optimal designs were computed by determining an analytical approximation the FIM for each Markov component (one where the previous state was awake and another where the previous state was asleep). Each Markov component FIM was weighted either equally or by the average probability of response being awake or asleep over the night and summed to derive the total FIM (FIM(total)). The reference designs were placebo, 0.1, 1-, 6-, 10- and 20-mg dosing for a 2- to 6-way crossover study in six dosing groups. Optimized design variables were dose and number of subjects in each dose group. The designs were validated using stochastic simulation/re-estimation (SSE). Contrary to expectations, the predicted parameter uncertainty obtained via FIM(total) was larger than the uncertainty in parameter estimates computed by SSE. Nevertheless, the D-optimal designs decreased the uncertainty of parameter estimates relative to the reference designs. Additionally, the improvement for the D-optimal designs were more pronounced using SSE than predicted via FIM(total). Through the use of an approximate analytic solution and weighting schemes, the FIM(total) for a non-homogeneous, dichotomous Markov-chain phase advanced sleep model was computed and provided more efficient trial designs and increased nonlinear mixed-effects modeling parameter precision.

(abstract) Modeling Protein Families and Human Genes: Hidden Markov Models and a Little Beyond

NASA Technical Reports Server (NTRS)

Baldi, Pierre

1994-01-01

We will first give a brief overview of Hidden Markov Models (HMMs) and their use in Computational Molecular Biology. In particular, we will describe a detailed application of HMMs to the G-Protein-Coupled-Receptor Superfamily. We will also describe a number of analytical results on HMMs that can be used in discrimination tests and database mining. We will then discuss the limitations of HMMs and some new directions of research. We will conclude with some recent results on the application of HMMs to human gene modeling and parsing.

Persistence and ergodicity of plant disease model with markov conversion and impulsive toxicant input

NASA Astrophysics Data System (ADS)

Zhao, Wencai; Li, Juan; Zhang, Tongqian; Meng, Xinzhu; Zhang, Tonghua

2017-07-01

Taking into account of both white and colored noises, a stochastic mathematical model with impulsive toxicant input is formulated. Based on this model, we investigate dynamics, such as the persistence and ergodicity, of plant infectious disease model with Markov conversion in a polluted environment. The thresholds of extinction and persistence in mean are obtained. By using Lyapunov functions, we prove that the system is ergodic and has a stationary distribution under certain sufficient conditions. Finally, numerical simulations are employed to illustrate our theoretical analysis.

Markov switching of the electricity supply curve and power prices dynamics

NASA Astrophysics Data System (ADS)

Mari, Carlo; Cananà, Lucianna

2012-02-01

Regime-switching models seem to well capture the main features of power prices behavior in deregulated markets. In a recent paper, we have proposed an equilibrium methodology to derive electricity prices dynamics from the interplay between supply and demand in a stochastic environment. In particular, assuming that the supply function is described by a power law where the exponent is a two-state strictly positive Markov process, we derived a regime switching dynamics of power prices in which regime switches are induced by transitions between Markov states. In this paper, we provide a dynamical model to describe the random behavior of power prices where the only non-Brownian component of the motion is endogenously introduced by Markov transitions in the exponent of the electricity supply curve. In this context, the stochastic process driving the switching mechanism becomes observable, and we will show that the non-Brownian component of the dynamics induced by transitions from Markov states is responsible for jumps and spikes of very high magnitude. The empirical analysis performed on three Australian markets confirms that the proposed approach seems quite flexible and capable of incorporating the main features of power prices time-series, thus reproducing the first four moments of log-returns empirical distributions in a satisfactory way.

Estimation of customer lifetime value of a health insurance with interest rates obeying uniform distribution

NASA Astrophysics Data System (ADS)

Widyawan, A.; Pasaribu, U. S.; Henintyas, Permana, D.

2015-12-01

Nowadays some firms, including insurer firms, think that customer-centric services are better than product-centric ones in terms of marketing. Insurance firms will try to attract as many new customer as possible while maintaining existing customer. This causes the Customer Lifetime Value (CLV) becomes a very important thing. CLV are able to put customer into different segments and calculate the present value of a firm's relationship with its customer. Insurance customer will depend on the last service he or she can get. So if the service is bad now, then customer will not renew his contract though the service is very good at an erlier time. Because of this situation one suitable mathematical model for modeling customer's relationships and calculating their lifetime value is Markov Chain. In addition, the advantages of using Markov Chain Modeling is its high degree of flexibility. In 2000, Pfeifer and Carraway states that Markov Chain Modeling can be used for customer retention situation. In this situation, Markov Chain Modeling requires only two states, which are present customer and former ones. This paper calculates customer lifetime value in an insurance firm with two distinctive interest rates; the constant interest rate and uniform distribution of interest rates. The result shows that loyal customer and the customer who increase their contract value have the highest CLV.

Tropical geometry of statistical models.

PubMed

Pachter, Lior; Sturmfels, Bernd

2004-11-16

This article presents a unified mathematical framework for inference in graphical models, building on the observation that graphical models are algebraic varieties. From this geometric viewpoint, observations generated from a model are coordinates of a point in the variety, and the sum-product algorithm is an efficient tool for evaluating specific coordinates. Here, we address the question of how the solutions to various inference problems depend on the model parameters. The proposed answer is expressed in terms of tropical algebraic geometry. The Newton polytope of a statistical model plays a key role. Our results are applied to the hidden Markov model and the general Markov model on a binary tree.

Utah State University Global Assimilation of Ionospheric Measurements Gauss-Markov Kalman filter model of the ionosphere: Model description and validation

NASA Astrophysics Data System (ADS)

Scherliess, L.; Schunk, R. W.; Sojka, J. J.; Thompson, D. C.; Zhu, L.

2006-11-01

The Utah State University Gauss-Markov Kalman Filter (GMKF) was developed as part of the Global Assimilation of Ionospheric Measurements (GAIM) program. The GMKF uses a physics-based model of the ionosphere and a Gauss-Markov Kalman filter as a basis for assimilating a diverse set of real-time (or near real-time) observations. The physics-based model is the Ionospheric Forecast Model (IFM), which accounts for five ion species and covers the E region, F region, and the topside from 90 to 1400 km altitude. Within the GMKF, the IFM derived ionospheric densities constitute a background density field on which perturbations are superimposed based on the available data and their errors. In the current configuration, the GMKF assimilates slant total electron content (TEC) from a variable number of global positioning satellite (GPS) ground sites, bottomside electron density (Ne) profiles from a variable number of ionosondes, in situ Ne from four Defense Meteorological Satellite Program (DMSP) satellites, and nighttime line-of-sight ultraviolet (UV) radiances measured by satellites. To test the GMKF for real-time operations and to validate its ionospheric density specifications, we have tested the model performance for a variety of geophysical conditions. During these model runs various combination of data types and data quantities were assimilated. To simulate real-time operations, the model ran continuously and automatically and produced three-dimensional global electron density distributions in 15 min increments. In this paper we will describe the Gauss-Markov Kalman filter model and present results of our validation study, with an emphasis on comparisons with independent observations.

Improving Markov Chain Models for Road Profiles Simulation via Definition of States

DTIC Science & Technology

2012-04-01

wavelet transform in pavement profile analysis," Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility, vol. 47, no. 4...34Estimating Markov Transition Probabilities from Micro -Unit Data," Journal of the Royal Statistical Society. Series C (Applied Statistics), pp. 355-371

A Systematic Review of Studies Evaluating the Cost Utility of Screening High-Risk Populations for Latent Tuberculosis Infection.

PubMed

Campbell, Jonathon R; Sasitharan, Thenuga; Marra, Fawziah

2015-08-01

As tuberculosis screening trends to targeting high-risk populations, knowing the cost effectiveness of such screening is vital to decision makers. The purpose of this review was to compile cost-utility analyses evaluating latent tuberculosis infection (LTBI) screening in high-risk populations that used quality-adjusted life-years (QALYs) as their measure of effectiveness. A literature search of MEDLINE, EMBASE, Cochrane Database of Systematic Reviews, Web of Knowledge, and PubMed was performed from database start to November 2014. Studies performed in populations at high risk of LTBI and subsequent reactivation that used the QALY as an effectiveness measure were included. Quality was assessed using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. Data extracted included tuberculin skin test (TST) and/or interferon-gamma release assay (IGRA) use, economic, screening, treatment, health state, and epidemiologic parameters. Data were summarized in regard to consistency in model parameters and the incremental cost-effectiveness ratio (ICER), with costs adjusted to 2013 US dollars. Of 415 studies identified, ultimately eight studies were included in the review. Most took a societal perspective (n = 4), used lifetime time horizons (n = 6), and used Markov models (n = 8). Screening of adult immigrants was found to be cost effective with a TST in one study, but moderately cost effective with an IGRA in another study; screening immigrants arriving more than 5 years prior with an IGRA was moderately cost effective until 44 years of age (n = 1). Screening HIV-positive patients was highly cost effective with a TST (n = 1) and moderately cost effective with an IGRA (n = 1). Screening in those with renal diseases (n = 2) and diabetes (n = 1) was not cost effective. Very few studies used the QALY as their effectiveness measure. Parameter and study design inconsistencies limit the comparability of studies. With validity issues in terms of parameters and assumptions, any conclusion should be interpreted with caution. Despite this, some cautionary recommendations emerged: screening HIV patients with a TST is highly cost effective, while screening adult immigrants with an IGRA is moderately cost effective.

Economic modeling of HIV treatments.

PubMed

Simpson, Kit N

2010-05-01

To review the general literature on microeconomic modeling and key points that must be considered in the general assessment of economic modeling reports, discuss the evolution of HIV economic models and identify models that illustrate this development over time, as well as examples of current studies. Recommend improvements in HIV economic modeling. Recent economic modeling studies of HIV include examinations of scaling up antiretroviral (ARV) in South Africa, screening prior to use of abacavir, preexposure prophylaxis, early start of ARV in developing countries and cost-effectiveness comparisons of specific ARV drugs using data from clinical trials. These studies all used extensively published second-generation Markov models in their analyses. There have been attempts to simplify approaches to cost-effectiveness estimates by using simple decision trees or cost-effectiveness calculations with short-time horizons. However, these approaches leave out important cumulative economic effects that will not appear early in a treatment. Many economic modeling studies were identified in the 'gray' literature, but limited descriptions precluded an assessment of their adherence to modeling guidelines, and thus to the validity of their findings. There is a need for developing third-generation models to accommodate new knowledge about adherence, adverse effects, and viral resistance.

Probability, statistics, and computational science.

PubMed

Beerenwinkel, Niko; Siebourg, Juliane

2012-01-01

In this chapter, we review basic concepts from probability theory and computational statistics that are fundamental to evolutionary genomics. We provide a very basic introduction to statistical modeling and discuss general principles, including maximum likelihood and Bayesian inference. Markov chains, hidden Markov models, and Bayesian network models are introduced in more detail as they occur frequently and in many variations in genomics applications. In particular, we discuss efficient inference algorithms and methods for learning these models from partially observed data. Several simple examples are given throughout the text, some of which point to models that are discussed in more detail in subsequent chapters.

ModFossa: A library for modeling ion channels using Python.

PubMed

Ferneyhough, Gareth B; Thibealut, Corey M; Dascalu, Sergiu M; Harris, Frederick C

2016-06-01

The creation and simulation of ion channel models using continuous-time Markov processes is a powerful and well-used tool in the field of electrophysiology and ion channel research. While several software packages exist for the purpose of ion channel modeling, most are GUI based, and none are available as a Python library. In an attempt to provide an easy-to-use, yet powerful Markov model-based ion channel simulator, we have developed ModFossa, a Python library supporting easy model creation and stimulus definition, complete with a fast numerical solver, and attractive vector graphics plotting.

A Stable Clock Error Model Using Coupled First and Second Order Gauss-Markov Processes

NASA Technical Reports Server (NTRS)

Carpenter, Russell; Lee, Taesul

2008-01-01

Long data outages may occur in applications of global navigation satellite system technology to orbit determination for missions that spend significant fractions of their orbits above the navigation satellite constellation(s). Current clock error models based on the random walk idealization may not be suitable in these circumstances, since the covariance of the clock errors may become large enough to overflow flight computer arithmetic. A model that is stable, but which approximates the existing models over short time horizons is desirable. A coupled first- and second-order Gauss-Markov process is such a model.

Estimation of Survival Probabilities for Use in Cost-effectiveness Analyses: A Comparison of a Multi-state Modeling Survival Analysis Approach with Partitioned Survival and Markov Decision-Analytic Modeling

PubMed Central

Williams, Claire; Lewsey, James D.; Mackay, Daniel F.; Briggs, Andrew H.

2016-01-01

Modeling of clinical-effectiveness in a cost-effectiveness analysis typically involves some form of partitioned survival or Markov decision-analytic modeling. The health states progression-free, progression and death and the transitions between them are frequently of interest. With partitioned survival, progression is not modeled directly as a state; instead, time in that state is derived from the difference in area between the overall survival and the progression-free survival curves. With Markov decision-analytic modeling, a priori assumptions are often made with regard to the transitions rather than using the individual patient data directly to model them. This article compares a multi-state modeling survival regression approach to these two common methods. As a case study, we use a trial comparing rituximab in combination with fludarabine and cyclophosphamide v. fludarabine and cyclophosphamide alone for the first-line treatment of chronic lymphocytic leukemia. We calculated mean Life Years and QALYs that involved extrapolation of survival outcomes in the trial. We adapted an existing multi-state modeling approach to incorporate parametric distributions for transition hazards, to allow extrapolation. The comparison showed that, due to the different assumptions used in the different approaches, a discrepancy in results was evident. The partitioned survival and Markov decision-analytic modeling deemed the treatment cost-effective with ICERs of just over £16,000 and £13,000, respectively. However, the results with the multi-state modeling were less conclusive, with an ICER of just over £29,000. This work has illustrated that it is imperative to check whether assumptions are realistic, as different model choices can influence clinical and cost-effectiveness results. PMID:27698003

Estimation of Survival Probabilities for Use in Cost-effectiveness Analyses: A Comparison of a Multi-state Modeling Survival Analysis Approach with Partitioned Survival and Markov Decision-Analytic Modeling.

PubMed

Williams, Claire; Lewsey, James D; Mackay, Daniel F; Briggs, Andrew H

2017-05-01

Modeling of clinical-effectiveness in a cost-effectiveness analysis typically involves some form of partitioned survival or Markov decision-analytic modeling. The health states progression-free, progression and death and the transitions between them are frequently of interest. With partitioned survival, progression is not modeled directly as a state; instead, time in that state is derived from the difference in area between the overall survival and the progression-free survival curves. With Markov decision-analytic modeling, a priori assumptions are often made with regard to the transitions rather than using the individual patient data directly to model them. This article compares a multi-state modeling survival regression approach to these two common methods. As a case study, we use a trial comparing rituximab in combination with fludarabine and cyclophosphamide v. fludarabine and cyclophosphamide alone for the first-line treatment of chronic lymphocytic leukemia. We calculated mean Life Years and QALYs that involved extrapolation of survival outcomes in the trial. We adapted an existing multi-state modeling approach to incorporate parametric distributions for transition hazards, to allow extrapolation. The comparison showed that, due to the different assumptions used in the different approaches, a discrepancy in results was evident. The partitioned survival and Markov decision-analytic modeling deemed the treatment cost-effective with ICERs of just over £16,000 and £13,000, respectively. However, the results with the multi-state modeling were less conclusive, with an ICER of just over £29,000. This work has illustrated that it is imperative to check whether assumptions are realistic, as different model choices can influence clinical and cost-effectiveness results.

Comparison of type 2 diabetes prevalence estimates in Saudi Arabia from a validated Markov model against the International Diabetes Federation and other modelling studies

PubMed Central

Al-Quwaidhi, Abdulkareem J.; Pearce, Mark S.; Sobngwi, Eugene; Critchley, Julia A.; O’Flaherty, Martin

2014-01-01

Aims To compare the estimates and projections of type 2 diabetes mellitus (T2DM) prevalence in Saudi Arabia from a validated Markov model against other modelling estimates, such as those produced by the International Diabetes Federation (IDF) Diabetes Atlas and the Global Burden of Disease (GBD) project. Methods A discrete-state Markov model was developed and validated that integrates data on population, obesity and smoking prevalence trends in adult Saudis aged ≥25 years to estimate the trends in T2DM prevalence (annually from 1992 to 2022). The model was validated by comparing the age- and sex-specific prevalence estimates against a national survey conducted in 2005. Results Prevalence estimates from this new Markov model were consistent with the 2005 national survey and very similar to the GBD study estimates. Prevalence in men and women in 2000 was estimated by the GBD model respectively at 17.5% and 17.7%, compared to 17.7% and 16.4% in this study. The IDF estimates of the total diabetes prevalence were considerably lower at 16.7% in 2011 and 20.8% in 2030, compared with 29.2% in 2011 and 44.1% in 2022 in this study. Conclusion In contrast to other modelling studies, both the Saudi IMPACT Diabetes Forecast Model and the GBD model directly incorporated the trends in obesity prevalence and/or body mass index (BMI) to inform T2DM prevalence estimates. It appears that such a direct incorporation of obesity trends in modelling studies results in higher estimates of the future prevalence of T2DM, at least in countries where obesity has been rapidly increasing. PMID:24447810

Using a genetic, observational study as a strategy to estimate the potential cost-effectiveness of pharmacological CCR5 blockade in dialysis patients.

PubMed

Muntinghe, Friso L H; Vegter, Stefan; Verduijn, Marion; Boeschoten, Elisabeth W; Dekker, Friedo W; Navis, Gerjan; Postma, Maarten

2011-07-01

Randomized clinical trials are expensive and time consuming. Therefore, strategies are needed to prioritise tracks for drug development. Genetic association studies may provide such a strategy by considering the differences between genotypes as a proxy for a natural, lifelong, randomized at conception, clinical trial. Previously an association with better survival was found in dialysis patients with systemic inflammation carrying a deletion variant of the CC-chemokine receptor 5 (CCR5). We hypothesized that in an analogous manner, pharmacological CCR5 blockade could protect against inflammation-driven mortality and estimated if such a treatment would be cost-effective. A genetic screen and treat strategy was modelled using a decision-analytic Markov model, in which patients were screened for the CCR5 deletion 32 polymorphism and those with the wild type and systemic inflammation were treated with pharmacological CCR5 blockers. Kidney transplantation and mortality rates were calculated using patient level data. Extensive sensitivity analyses were performed. The cost-effectiveness of the genetic screen and treat strategy was &OV0556;18 557 per life year gained and &OV0556;21 896 per quality-adjusted life years gained. Concordance between the genetic association and pharmacological effectiveness was a main driver of cost-effectiveness. Sensitivity analyses showed that even a modest effectiveness of pharmacological CCR5 blockade would result in a treatment strategy that is good value for money. Pharmacological blockade of the CCR5 receptor in inflamed dialysis patients can be incorporated in a potentially cost-effective screen and treat programme. These findings provide formal rationale for clinical studies. This study illustrates the potential of genetic association studies for drug development, as a source of Mendelian randomized evidence from an observational setting.

Cost-effectiveness of magnetic resonance imaging with a new contrast agent for the early diagnosis of Alzheimer's disease.

PubMed

Biasutti, Maria; Dufour, Natacha; Ferroud, Clotilde; Dab, William; Temime, Laura

2012-01-01

Used as contrast agents for brain magnetic resonance imaging (MRI), markers for beta-amyloid deposits might allow early diagnosis of Alzheimer's disease (AD). We evaluated the cost-effectiveness of such a diagnostic test, MRI+CLP (contrastophore-linker-pharmacophore), should it become clinically available. We compared the cost-effectiveness of MRI+CLP to that of standard diagnosis using currently available cognition tests and of standard MRI, and investigated the impact of a hypothetical treatment efficient in early AD. The primary analysis was based on the current French context for 70-year-old patients with Mild Cognitive Impairment (MCI). In alternative "screen and treat" scenarios, we analyzed the consequences of systematic screenings of over-60 individuals (either population-wide or restricted to the ApoE4 genotype population). We used a Markov model of AD progression; model parameters, as well as incurred costs and quality-of-life weights in France were taken from the literature. We performed univariate and probabilistic multivariate sensitivity analyses. The base-case preferred strategy was the standard MRI diagnosis strategy. In the primary analysis however, MRI+CLP could become the preferred strategy under a wide array of scenarios involving lower cost and/or higher sensitivity or specificity. By contrast, in the "screen and treat" analyses, the probability of MRI+CLP becoming the preferred strategy remained lower than 5%. It is thought that anti-beta-amyloid compounds might halt the development of dementia in early stage patients. This study suggests that, even should such treatments become available, systematically screening the over-60 population for AD would only become cost-effective with highly specific tests able to diagnose early stages of the disease. However, offering a new diagnostic test based on beta-amyloid markers to elderly patients with MCI might prove cost-effective.

Designing small molecules to target cryptic pockets yields both positive and negative allosteric modulators

PubMed Central

Moeder, Katelyn E.; Ho, Chris M. W.; Zimmerman, Maxwell I.; Frederick, Thomas E.; Bowman, Gregory R.

2017-01-01

Allosteric drugs, which bind to proteins in regions other than their main ligand-binding or active sites, make it possible to target proteins considered “undruggable” and to develop new therapies that circumvent existing resistance. Despite growing interest in allosteric drug discovery, rational design is limited by a lack of sufficient structural information about alternative binding sites in proteins. Previously, we used Markov State Models (MSMs) to identify such “cryptic pockets,” and here we describe a method for identifying compounds that bind in these cryptic pockets and modulate enzyme activity. Experimental tests validate our approach by revealing both an inhibitor and two activators of TEM β-lactamase (TEM). To identify hits, a library of compounds is first virtually screened against either the crystal structure of a known cryptic pocket or an ensemble of structures containing the same cryptic pocket that is extracted from an MSM. Hit compounds are then screened experimentally and characterized kinetically in individual assays. We identify three hits, one inhibitor and two activators, demonstrating that screening for binding to allosteric sites can result in both positive and negative modulation. The hit compounds have modest effects on TEM activity, but all have higher affinities than previously identified inhibitors, which bind the same cryptic pocket but were found, by chance, via a computational screen targeting the active site. Site-directed mutagenesis of key contact residues predicted by the docking models is used to confirm that the compounds bind in the cryptic pocket as intended. Because hit compounds are identified from docking against both the crystal structure and structures from the MSM, this platform should prove suitable for many proteins, particularly targets whose crystal structures lack obvious druggable pockets, and for identifying both inhibitory and activating small-molecule modulators. PMID:28570708

Detecting Coevolution in and among Protein Domains

PubMed Central

Yeang, Chen-Hsiang; Haussler, David

2007-01-01

Correlated changes of nucleic or amino acids have provided strong information about the structures and interactions of molecules. Despite the rich literature in coevolutionary sequence analysis, previous methods often have to trade off between generality, simplicity, phylogenetic information, and specific knowledge about interactions. Furthermore, despite the evidence of coevolution in selected protein families, a comprehensive screening of coevolution among all protein domains is still lacking. We propose an augmented continuous-time Markov process model for sequence coevolution. The model can handle different types of interactions, incorporate phylogenetic information and sequence substitution, has only one extra free parameter, and requires no knowledge about interaction rules. We employ this model to large-scale screenings on the entire protein domain database (Pfam). Strikingly, with 0.1 trillion tests executed, the majority of the inferred coevolving protein domains are functionally related, and the coevolving amino acid residues are spatially coupled. Moreover, many of the coevolving positions are located at functionally important sites of proteins/protein complexes, such as the subunit linkers of superoxide dismutase, the tRNA binding sites of ribosomes, the DNA binding region of RNA polymerase, and the active and ligand binding sites of various enzymes. The results suggest sequence coevolution manifests structural and functional constraints of proteins. The intricate relations between sequence coevolution and various selective constraints are worth pursuing at a deeper level. PMID:17983264

Sentiment classification technology based on Markov logic networks

NASA Astrophysics Data System (ADS)

He, Hui; Li, Zhigang; Yao, Chongchong; Zhang, Weizhe

2016-07-01

With diverse online media emerging, there is a growing concern of sentiment classification problem. At present, text sentiment classification mainly utilizes supervised machine learning methods, which feature certain domain dependency. On the basis of Markov logic networks (MLNs), this study proposed a cross-domain multi-task text sentiment classification method rooted in transfer learning. Through many-to-one knowledge transfer, labeled text sentiment classification, knowledge was successfully transferred into other domains, and the precision of the sentiment classification analysis in the text tendency domain was improved. The experimental results revealed the following: (1) the model based on a MLN demonstrated higher precision than the single individual learning plan model. (2) Multi-task transfer learning based on Markov logical networks could acquire more knowledge than self-domain learning. The cross-domain text sentiment classification model could significantly improve the precision and efficiency of text sentiment classification.

Optimal use of colonoscopy and fecal immunochemical test for population-based colorectal cancer screening: a cost-effectiveness analysis using Japanese data.

PubMed

Sekiguchi, Masau; Igarashi, Ataru; Matsuda, Takahisa; Matsumoto, Minori; Sakamoto, Taku; Nakajima, Takeshi; Kakugawa, Yasuo; Yamamoto, Seiichiro; Saito, Hiroshi; Saito, Yutaka

2016-02-01

There have been few cost-effectiveness analyses of population-based colorectal cancer screening in Japan, and there is no consensus on the optimal use of total colonoscopy and the fecal immunochemical test for colorectal cancer screening with regard to cost-effectiveness and total colonoscopy workload. The present study aimed to examine the cost-effectiveness of colorectal cancer screening using Japanese data to identify the optimal use of total colonoscopy and fecal immunochemical test. We developed a Markov model to assess the cost-effectiveness of colorectal cancer screening offered to an average-risk population aged 40 years or over. The cost, quality-adjusted life-years and number of total colonoscopy procedures required were evaluated for three screening strategies: (i) a fecal immunochemical test-based strategy; (ii) a total colonoscopy-based strategy; (iii) a strategy of adding population-wide total colonoscopy at 50 years to a fecal immunochemical test-based strategy. All three strategies dominated no screening. Among the three, Strategy 1 was dominated by Strategy 3, and the incremental cost per quality-adjusted life-years gained for Strategy 2 against Strategies 1 and 3 were JPY 293 616 and JPY 781 342, respectively. Within the Japanese threshold (JPY 5-6 million per QALY gained), Strategy 2 was the most cost-effective, followed by Strategy 3; however, Strategy 2 required more than double the number of total colonoscopy procedures than the other strategies. The total colonoscopy-based strategy could be the most cost-effective for population-based colorectal cancer screening in Japan. However, it requires more total colonoscopy procedures than the other strategies. Depending on total colonoscopy capacity, the strategy of adding total colonoscopy for individuals at a specified age to a fecal immunochemical test-based screening may be an optimal solution. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Information-Theoretic Performance Analysis of Sensor Networks via Markov Modeling of Time Series Data.

PubMed

Li, Yue; Jha, Devesh K; Ray, Asok; Wettergren, Thomas A; Yue Li; Jha, Devesh K; Ray, Asok; Wettergren, Thomas A; Wettergren, Thomas A; Li, Yue; Ray, Asok; Jha, Devesh K

2018-06-01

This paper presents information-theoretic performance analysis of passive sensor networks for detection of moving targets. The proposed method falls largely under the category of data-level information fusion in sensor networks. To this end, a measure of information contribution for sensors is formulated in a symbolic dynamics framework. The network information state is approximately represented as the largest principal component of the time series collected across the network. To quantify each sensor's contribution for generation of the information content, Markov machine models as well as x-Markov (pronounced as cross-Markov) machine models, conditioned on the network information state, are constructed; the difference between the conditional entropies of these machines is then treated as an approximate measure of information contribution by the respective sensors. The x-Markov models represent the conditional temporal statistics given the network information state. The proposed method has been validated on experimental data collected from a local area network of passive sensors for target detection, where the statistical characteristics of environmental disturbances are similar to those of the target signal in the sense of time scale and texture. A distinctive feature of the proposed algorithm is that the network decisions are independent of the behavior and identity of the individual sensors, which is desirable from computational perspectives. Results are presented to demonstrate the proposed method's efficacy to correctly identify the presence of a target with very low false-alarm rates. The performance of the underlying algorithm is compared with that of a recent data-driven, feature-level information fusion algorithm. It is shown that the proposed algorithm outperforms the other algorithm.

Monte Carlo Simulation of Markov, Semi-Markov, and Generalized Semi- Markov Processes in Probabilistic Risk Assessment

NASA Technical Reports Server (NTRS)

English, Thomas

2005-01-01

A standard tool of reliability analysis used at NASA-JSC is the event tree. An event tree is simply a probability tree, with the probabilities determining the next step through the tree specified at each node. The nodal probabilities are determined by a reliability study of the physical system at work for a particular node. The reliability study performed at a node is typically referred to as a fault tree analysis, with the potential of a fault tree existing.for each node on the event tree. When examining an event tree it is obvious why the event tree/fault tree approach has been adopted. Typical event trees are quite complex in nature, and the event tree/fault tree approach provides a systematic and organized approach to reliability analysis. The purpose of this study was two fold. Firstly, we wanted to explore the possibility that a semi-Markov process can create dependencies between sojourn times (the times it takes to transition from one state to the next) that can decrease the uncertainty when estimating time to failures. Using a generalized semi-Markov model, we studied a four element reliability model and were able to demonstrate such sojourn time dependencies. Secondly, we wanted to study the use of semi-Markov processes to introduce a time variable into the event tree diagrams that are commonly developed in PRA (Probabilistic Risk Assessment) analyses. Event tree end states which change with time are more representative of failure scenarios than are the usual static probability-derived end states.

Incremental cost-effectiveness of screening and laser treatment for diabetic retinopathy and macular edema in Malawi.

PubMed

Vetrini, Damir; Kiire, Christine A; Burgess, Philip I; Harding, Simon P; Kayange, Petros C; Kalua, Khumbo; Msukwa, Gerald; Beare, Nicholas A V; Madan, Jason

2018-01-01

To investigate the economic impact of introducing targeted screening and laser photocoagulation treatment for sight-threatening diabetic retinopathy and macular edema in a setting with no previous screening or laser treatment for diabetic retinopathy in sub-Saharan Africa. A cohort Markov model was built to compare combined targeted screening and laser treatment for patients with sight-threatening diabetic retinopathy and macular edema against no intervention. Primary outcomes were incremental cost per quality-adjusted life year (QALY) gained and per disability-adjusted life year (DALY) averted. Primary data were collected on 357 participants from the Malawi Diabetic Retinopathy Study, a prospective, observational cohort study. Multiple scenarios were explored and a probabilistic sensitivity analysis was performed. In the base case (age: 50 years, service utilization rate: 80%), the cost of the intervention and the years of severe visual impairment averted per patient screened were $209 and 2.2 years respectively. Applying the World Health Organization threshold of cost-effectiveness for Malawi ($679), the base case was cost-effective when QALYs were used ($400 per QALY gained) but not when DALYs were used ($766 per DALY averted). The intervention was more cost-effective when it targeted younger patients (age: 30 years) and less cost-effective when the utilization rate was lowered to 50%. Annual photographic screening of diabetic patients attending medical diabetes clinics in Malawi, with the provision of laser treatment for those with sight-threatening diabetic retinopathy and macular edema, appears to be cost-effective in terms of QALYs gained, in our base case scenario. Cost-effectiveness improves if services are utilized more intensively and extended to younger patients.

Screening and prevention of venous thromboembolism in critically ill patients: a decision analysis and economic evaluation.

PubMed

Sud, Sachin; Mittmann, Nicole; Cook, Deborah J; Geerts, William; Chan, Brian; Dodek, Peter; Gould, Michael K; Guyatt, Gordon; Arabi, Yaseen; Fowler, Robert A

2011-12-01

Venous thromboembolism is difficult to diagnose in critically ill patients and may increase morbidity and mortality. To evaluate the cost-effectiveness of strategies to reduce morbidity from venous thromboembolism in critically ill patients. A Markov decision analytic model to compare weekly compression ultrasound screening (screening) plus investigation for clinically suspected deep vein thrombosis (DVT) (case finding) versus case finding alone; and a hypothetical program to increase adherence to DVT prevention. Probabilities were derived from a systematic review of venous thromboembolism in medical-surgical intensive care unit patients. Costs (in 2010 $US) were obtained from hospitals in Canada, Australia, and the United States, and the medical literature. Analyses were conducted from a societal perspective over a lifetime horizon. Outcomes included costs, quality-adjusted life-years (QALY), and incremental cost-effectiveness ratios. In the base case, the rate of proximal DVT was 85 per 1,000 patients. Screening resulted in three fewer pulmonary emboli than case-finding alone but also two additional bleeding episodes, and cost $223,801 per QALY gained. In sensitivity analyses, screening cost less than $50,000 per QALY only if the probability of proximal DVT increased from a baseline of 8.5-16%. By comparison, increasing adherence to appropriate pharmacologic thromboprophylaxis by 10% resulted in 16 fewer DVTs, one fewer pulmonary emboli, and one additional heparin-induced thrombocytopenia and bleeding event, and cost $27,953 per QALY gained. Programs achieving increased adherence to best-practice venous thromboembolism prevention were cost-effective over a wide range of program costs and were robust in probabilistic sensitivity analyses. Appropriate prophylaxis provides better value in terms of costs and health gains than routine screening for DVT. Resources should be targeted at optimizing thromboprophylaxis.

Modeling dyadic processes using Hidden Markov Models: A time series approach to mother-infant interactions during infant immunization.

PubMed

Stifter, Cynthia A; Rovine, Michael

2015-01-01

The focus of the present longitudinal study, to examine mother-infant interaction during the administration of immunizations at two and six months of age, used hidden Markov modeling, a time series approach that produces latent states to describe how mothers and infants work together to bring the infant to a soothed state. Results revealed a 4-state model for the dyadic responses to a two-month inoculation whereas a 6-state model best described the dyadic process at six months. Two of the states at two months and three of the states at six months suggested a progression from high intensity crying to no crying with parents using vestibular and auditory soothing methods. The use of feeding and/or pacifying to soothe the infant characterized one two-month state and two six-month states. These data indicate that with maturation and experience, the mother-infant dyad is becoming more organized around the soothing interaction. Using hidden Markov modeling to describe individual differences, as well as normative processes, is also presented and discussed.

GPU-powered Shotgun Stochastic Search for Dirichlet process mixtures of Gaussian Graphical Models

PubMed Central

Mukherjee, Chiranjit; Rodriguez, Abel

2016-01-01

Gaussian graphical models are popular for modeling high-dimensional multivariate data with sparse conditional dependencies. A mixture of Gaussian graphical models extends this model to the more realistic scenario where observations come from a heterogenous population composed of a small number of homogeneous sub-groups. In this paper we present a novel stochastic search algorithm for finding the posterior mode of high-dimensional Dirichlet process mixtures of decomposable Gaussian graphical models. Further, we investigate how to harness the massive thread-parallelization capabilities of graphical processing units to accelerate computation. The computational advantages of our algorithms are demonstrated with various simulated data examples in which we compare our stochastic search with a Markov chain Monte Carlo algorithm in moderate dimensional data examples. These experiments show that our stochastic search largely outperforms the Markov chain Monte Carlo algorithm in terms of computing-times and in terms of the quality of the posterior mode discovered. Finally, we analyze a gene expression dataset in which Markov chain Monte Carlo algorithms are too slow to be practically useful. PMID:28626348

GPU-powered Shotgun Stochastic Search for Dirichlet process mixtures of Gaussian Graphical Models.

PubMed

Mukherjee, Chiranjit; Rodriguez, Abel

2016-01-01

Gaussian graphical models are popular for modeling high-dimensional multivariate data with sparse conditional dependencies. A mixture of Gaussian graphical models extends this model to the more realistic scenario where observations come from a heterogenous population composed of a small number of homogeneous sub-groups. In this paper we present a novel stochastic search algorithm for finding the posterior mode of high-dimensional Dirichlet process mixtures of decomposable Gaussian graphical models. Further, we investigate how to harness the massive thread-parallelization capabilities of graphical processing units to accelerate computation. The computational advantages of our algorithms are demonstrated with various simulated data examples in which we compare our stochastic search with a Markov chain Monte Carlo algorithm in moderate dimensional data examples. These experiments show that our stochastic search largely outperforms the Markov chain Monte Carlo algorithm in terms of computing-times and in terms of the quality of the posterior mode discovered. Finally, we analyze a gene expression dataset in which Markov chain Monte Carlo algorithms are too slow to be practically useful.

Modeling dyadic processes using Hidden Markov Models: A time series approach to mother-infant interactions during infant immunization

PubMed Central

Stifter, Cynthia A.; Rovine, Michael

2016-01-01

The focus of the present longitudinal study, to examine mother-infant interaction during the administration of immunizations at two and six months of age, used hidden Markov modeling, a time series approach that produces latent states to describe how mothers and infants work together to bring the infant to a soothed state. Results revealed a 4-state model for the dyadic responses to a two-month inoculation whereas a 6-state model best described the dyadic process at six months. Two of the states at two months and three of the states at six months suggested a progression from high intensity crying to no crying with parents using vestibular and auditory soothing methods. The use of feeding and/or pacifying to soothe the infant characterized one two-month state and two six-month states. These data indicate that with maturation and experience, the mother-infant dyad is becoming more organized around the soothing interaction. Using hidden Markov modeling to describe individual differences, as well as normative processes, is also presented and discussed. PMID:27284272

A Markov chain model for studying suicide dynamics: an illustration of the Rose theorem

PubMed Central

2014-01-01

Background High-risk strategies would only have a modest effect on suicide prevention within a population. It is best to incorporate both high-risk and population-based strategies to prevent suicide. This study aims to compare the effectiveness of suicide prevention between high-risk and population-based strategies. Methods A Markov chain illness and death model is proposed to determine suicide dynamic in a population and examine its effectiveness for reducing the number of suicides by modifying certain parameters of the model. Assuming a population with replacement, the suicide risk of the population was estimated by determining the final state of the Markov model. Results The model shows that targeting the whole population for suicide prevention is more effective than reducing risk in the high-risk tail of the distribution of psychological distress (i.e. the mentally ill). Conclusions The results of this model reinforce the essence of the Rose theorem that lowering the suicidal risk in the population at large may be more effective than reducing the high risk in a small population. PMID:24948330

Optimal choice of word length when comparing two Markov sequences using a χ 2-statistic.

PubMed

Bai, Xin; Tang, Kujin; Ren, Jie; Waterman, Michael; Sun, Fengzhu

2017-10-03

Alignment-free sequence comparison using counts of word patterns (grams, k-tuples) has become an active research topic due to the large amount of sequence data from the new sequencing technologies. Genome sequences are frequently modelled by Markov chains and the likelihood ratio test or the corresponding approximate χ 2 -statistic has been suggested to compare two sequences. However, it is not known how to best choose the word length k in such studies. We develop an optimal strategy to choose k by maximizing the statistical power of detecting differences between two sequences. Let the orders of the Markov chains for the two sequences be r 1 and r 2 , respectively. We show through both simulations and theoretical studies that the optimal k= max(r 1 ,r 2 )+1 for both long sequences and next generation sequencing (NGS) read data. The orders of the Markov chains may be unknown and several methods have been developed to estimate the orders of Markov chains based on both long sequences and NGS reads. We study the power loss of the statistics when the estimated orders are used. It is shown that the power loss is minimal for some of the estimators of the orders of Markov chains. Our studies provide guidelines on choosing the optimal word length for the comparison of Markov sequences.

An open Markov chain scheme model for a credit consumption portfolio fed by ARIMA and SARMA processes

NASA Astrophysics Data System (ADS)

Esquível, Manuel L.; Fernandes, José Moniz; Guerreiro, Gracinda R.

2016-06-01

We introduce a schematic formalism for the time evolution of a random population entering some set of classes and such that each member of the population evolves among these classes according to a scheme based on a Markov chain model. We consider that the flow of incoming members is modeled by a time series and we detail the time series structure of the elements in each of the classes. We present a practical application to data from a credit portfolio of a Cape Verdian bank; after modeling the entering population in two different ways - namely as an ARIMA process and as a deterministic sigmoid type trend plus a SARMA process for the residues - we simulate the behavior of the population and compare the results. We get that the second method is more accurate in describing the behavior of the populations when compared to the observed values in a direct simulation of the Markov chain.

Monitoring volcano activity through Hidden Markov Model

NASA Astrophysics Data System (ADS)

Cassisi, C.; Montalto, P.; Prestifilippo, M.; Aliotta, M.; Cannata, A.; Patanè, D.

2013-12-01

During 2011-2013, Mt. Etna was mainly characterized by cyclic occurrences of lava fountains, totaling to 38 episodes. During this time interval Etna volcano's states (QUIET, PRE-FOUNTAIN, FOUNTAIN, POST-FOUNTAIN), whose automatic recognition is very useful for monitoring purposes, turned out to be strongly related to the trend of RMS (Root Mean Square) of the seismic signal recorded by stations close to the summit area. Since RMS time series behavior is considered to be stochastic, we can try to model the system generating its values, assuming to be a Markov process, by using Hidden Markov models (HMMs). HMMs are a powerful tool in modeling any time-varying series. HMMs analysis seeks to recover the sequence of hidden states from the observed emissions. In our framework, observed emissions are characters generated by the SAX (Symbolic Aggregate approXimation) technique, which maps RMS time series values with discrete literal emissions. The experiments show how it is possible to guess volcano states by means of HMMs and SAX.

Cost-Effectiveness of Screening for Intermediate Age-Related Macular Degeneration during Diabetic Retinopathy Screening.

PubMed

Chan, Christina K W; Gangwani, Rita A; McGhee, Sarah M; Lian, JinXiao; Wong, David S H

2015-11-01

To determine whether screening for age-related macular degeneration (AMD) during a diabetic retinopathy (DR) screening program would be cost effective in Hong Kong. We compared and evaluated the impacts of screening, grading, and vitamin treatment for intermediate AMD compared with no screening using a Markov model. It was based on the natural history of AMD in a cohort with a mean age of 62 years, followed up until 100 years of age or death. Subjects attending a DR screening program were recruited. A cost-effectiveness analysis was undertaken from a public provider perspective. It included grading for AMD using the photographs obtained for DR screening and treatment with vitamin therapy for those with intermediate AMD. The measures of effectiveness were obtained largely from a local study, but the transition probabilities and utility values were from overseas data. Costs were all from local sources. The main assumptions and estimates were tested in sensitivity analyses. The outcome was cost per quality-adjusted life year (QALY) gained. Both costs and benefits were discounted at 3%. All costs are reported in United States dollars ($). The cost per QALY gained through screening for AMD and vitamin treatment for appropriate cases was $12,712 after discounting. This would be considered highly cost effective based on the World Health Organization's threshold of willingness to pay (WTP) for a QALY, that is, less than the annual per capita gross domestic product of $29,889. Because of uncertainty regarding the utility value for those with advanced AMD, we also tested an extreme, conservative value for utility under which screening remained cost effective. One-way sensitivity analyses revealed that, besides utility values, the cost per QALY was most sensitive to the progression rate from intermediate to advanced AMD. The cost-effectiveness acceptability curve showed a WTP for a QALY of $29,000 or more has a more than 86% probability of being cost effective compared with no screening. Our analysis demonstrated that AMD screening carried out simultaneously with DR screening for patients with diabetes would be cost effective in a Hong Kong public healthcare setting. Copyright © 2015 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

[Succession caused by beaver (Castor fiber L.) life activity: II. A refined Markov model].

PubMed

Logofet; Evstigneev, O I; Aleinikov, A A; Morozova, A O

2015-01-01

The refined Markov model of cyclic zoogenic successions caused by beaver (Castor fiber L.) life activity represents a discrete chain of the following six states: flooded forest, swamped forest, pond, grassy swamp, shrubby swamp, and wet forest, which correspond to certain stages of succession. Those stages are defined, and a conceptual scheme of probable transitions between them for one time step is constructed from the knowledge of beaver behaviour in small river floodplains of "Bryanskii Les" Reserve. We calibrated the corresponding matrix of transition probabilities according to the optimization principle: minimizing differences between the model outcome and reality; the model generates a distribution of relative areas corresponding to the stages of succession, that has to be compared to those gained from case studies in the Reserve during 2002-2006. The time step is chosen to equal 2 years, and the first-step data in the sum of differences are given various weights, w (between 0 and 1). The value of w = 0.2 is selected due to its optimality and for some additional reasons. By the formulae of finite homogeneous Markov chain theory, we obtained the main results of the calibrated model, namely, a steady-state distribution of stage areas, indexes of cyclicity, and the mean durations (M(j)) of succession stages. The results of calibration give an objective quantitative nature to the expert knowledge of the course of succession and get a proper interpretation. The 2010 data, which are not involved in the calibration procedure, enabled assessing the quality of prediction by the homogeneous model in short-term (from the 2006 situation): the error of model area distribution relative to the distribution observed in 2010 falls into the range of 9-17%, the best prognosis being given by the least optimal matrices (rejected values of w). This indicates a formally heterogeneous nature of succession processes in time. Thus, the refined version of the homogeneous Markov chain has not eliminated all the contradictions between the model results and expert knowledge, which suggests a further model development towards a "logically inhomogeneous" version or/and refusal to postulate the Markov property in the conceptual scheme of succession.

Dependability and performability analysis

NASA Technical Reports Server (NTRS)

Trivedi, Kishor S.; Ciardo, Gianfranco; Malhotra, Manish; Sahner, Robin A.

1993-01-01

Several practical issues regarding specifications and solution of dependability and performability models are discussed. Model types with and without rewards are compared. Continuous-time Markov chains (CTMC's) are compared with (continuous-time) Markov reward models (MRM's) and generalized stochastic Petri nets (GSPN's) are compared with stochastic reward nets (SRN's). It is shown that reward-based models could lead to more concise model specifications and solution of a variety of new measures. With respect to the solution of dependability and performability models, three practical issues were identified: largeness, stiffness, and non-exponentiality, and a variety of approaches are discussed to deal with them, including some of the latest research efforts.

Hidden markov model for the prediction of transmembrane proteins using MATLAB.

PubMed

Chaturvedi, Navaneet; Shanker, Sudhanshu; Singh, Vinay Kumar; Sinha, Dhiraj; Pandey, Paras Nath

2011-01-01

Since membranous proteins play a key role in drug targeting therefore transmembrane proteins prediction is active and challenging area of biological sciences. Location based prediction of transmembrane proteins are significant for functional annotation of protein sequences. Hidden markov model based method was widely applied for transmembrane topology prediction. Here we have presented a revised and a better understanding model than an existing one for transmembrane protein prediction. Scripting on MATLAB was built and compiled for parameter estimation of model and applied this model on amino acid sequence to know the transmembrane and its adjacent locations. Estimated model of transmembrane topology was based on TMHMM model architecture. Only 7 super states are defined in the given dataset, which were converted to 96 states on the basis of their length in sequence. Accuracy of the prediction of model was observed about 74 %, is a good enough in the area of transmembrane topology prediction. Therefore we have concluded the hidden markov model plays crucial role in transmembrane helices prediction on MATLAB platform and it could also be useful for drug discovery strategy. The database is available for free at bioinfonavneet@gmail.comvinaysingh@bhu.ac.in.

Recursive utility in a Markov environment with stochastic growth

PubMed Central

Hansen, Lars Peter; Scheinkman, José A.

2012-01-01

Recursive utility models that feature investor concerns about the intertemporal composition of risk are used extensively in applied research in macroeconomics and asset pricing. These models represent preferences as the solution to a nonlinear forward-looking difference equation with a terminal condition. In this paper we study infinite-horizon specifications of this difference equation in the context of a Markov environment. We establish a connection between the solution to this equation and to an arguably simpler Perron–Frobenius eigenvalue equation of the type that occurs in the study of large deviations for Markov processes. By exploiting this connection, we establish existence and uniqueness results. Moreover, we explore a substantive link between large deviation bounds for tail events for stochastic consumption growth and preferences induced by recursive utility. PMID:22778428

Recursive utility in a Markov environment with stochastic growth.

PubMed

Hansen, Lars Peter; Scheinkman, José A

2012-07-24

Recursive utility models that feature investor concerns about the intertemporal composition of risk are used extensively in applied research in macroeconomics and asset pricing. These models represent preferences as the solution to a nonlinear forward-looking difference equation with a terminal condition. In this paper we study infinite-horizon specifications of this difference equation in the context of a Markov environment. We establish a connection between the solution to this equation and to an arguably simpler Perron-Frobenius eigenvalue equation of the type that occurs in the study of large deviations for Markov processes. By exploiting this connection, we establish existence and uniqueness results. Moreover, we explore a substantive link between large deviation bounds for tail events for stochastic consumption growth and preferences induced by recursive utility.

Many roads to synchrony: natural time scales and their algorithms.

PubMed

James, Ryan G; Mahoney, John R; Ellison, Christopher J; Crutchfield, James P

2014-04-01

We consider two important time scales-the Markov and cryptic orders-that monitor how an observer synchronizes to a finitary stochastic process. We show how to compute these orders exactly and that they are most efficiently calculated from the ε-machine, a process's minimal unifilar model. Surprisingly, though the Markov order is a basic concept from stochastic process theory, it is not a probabilistic property of a process. Rather, it is a topological property and, moreover, it is not computable from any finite-state model other than the ε-machine. Via an exhaustive survey, we close by demonstrating that infinite Markov and infinite cryptic orders are a dominant feature in the space of finite-memory processes. We draw out the roles played in statistical mechanical spin systems by these two complementary length scales.

Application of Markov Models for Analysis of Development of Psychological Characteristics

ERIC Educational Resources Information Center

Kuravsky, Lev S.; Malykh, Sergey B.

2004-01-01

A technique to study combined influence of environmental and genetic factors on the base of changes in phenotype distributions is presented. Histograms are exploited as base analyzed characteristics. A continuous time, discrete state Markov process with piece-wise constant interstate transition rates is associated with evolution of each histogram.…

Markov Random Fields, Stochastic Quantization and Image Analysis

DTIC Science & Technology

1990-01-01

Markov random fields based on the lattice Z2 have been extensively used in image analysis in a Bayesian framework as a-priori models for the...of Image Analysis can be given some fundamental justification then there is a remarkable connection between Probabilistic Image Analysis , Statistical Mechanics and Lattice-based Euclidean Quantum Field Theory.

UMAP Modules-Units 105, 107-109, 111-112, 158-162.

ERIC Educational Resources Information Center

Keller, Mary K.; And Others

This collection of materials includes six units dealing with applications of matrix methods. These are: 105-Food Service Management; 107-Markov Chains; 108-Electrical Circuits; 109-Food Service and Dietary Requirements; 111-Fixed Point and Absorbing Markov Chains; and 112-Analysis of Linear Circuits. The units contain exercises and model exams,…

Predicting hepatitis B monthly incidence rates using weighted Markov chains and time series methods.

PubMed

Shahdoust, Maryam; Sadeghifar, Majid; Poorolajal, Jalal; Javanrooh, Niloofar; Amini, Payam

2015-01-01

Hepatitis B (HB) is a major global mortality. Accurately predicting the trend of the disease can provide an appropriate view to make health policy disease prevention. This paper aimed to apply three different to predict monthly incidence rates of HB. This historical cohort study was conducted on the HB incidence data of Hamadan Province, the west of Iran, from 2004 to 2012. Weighted Markov Chain (WMC) method based on Markov chain theory and two time series models including Holt Exponential Smoothing (HES) and SARIMA were applied on the data. The results of different applied methods were compared to correct percentages of predicted incidence rates. The monthly incidence rates were clustered into two clusters as state of Markov chain. The correct predicted percentage of the first and second clusters for WMC, HES and SARIMA methods was (100, 0), (84, 67) and (79, 47) respectively. The overall incidence rate of HBV is estimated to decrease over time. The comparison of results of the three models indicated that in respect to existing seasonality trend and non-stationarity, the HES had the most accurate prediction of the incidence rates.

Análisis de Costo-Efectividad de las Estrategias de Tamización de Cáncer Colorrectal en Colombia.

PubMed

Pinzon Florez, Carlos Eduardo; Rosselli, Diego; Gamboa Garay, Oscar Andrés

2012-12-01

To evaluate the cost-effectiveness of different screening strategies for colorectal cancer in Colombia. We designed a Markov model to compare the clinical and economic impact in terms of reducing the incidence and mortality from colorectal cancer (CRC). Six screening strategies for adults were compared: fecal occult blood (FOBT) immunochemical and guaiac type, conventional colonoscopy, flexible sigmoidoscopy, and FOBT guaiac and immunochemical type more sigmoidoscopy. We used the third-party payer perspective, including only direct costs, the time horizon was the life expectancy of the Colombian population. We estimated cost-effectiveness ratios (CERs) and incremental cost-effectiveness (ICER). Were performed deterministic sensitivity analysis and probabilistic. We applied a discount rate of 3% in the costs and health outcomes. The screening strategy more cost-effective was the FOBT biennial guaiac type. The cost per life year gained was US$10,347.37, US$18,380.64, and US$45,158.05. For FOBT guaiac biennial, FOBT guaiac annual and FOBT inmunoquímica biennial respectively. The ICER is sensitive to the percentage of false positive test for FOBT guaiac type values greater than 10%, and the cost of the test. The screening strategy more cost-effective for Colombia is the FOBT biennial guaiac type, using as a threshold the gross domestic product (GDP) per capita in Colombia. Copyright © 2012 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Developing a statistically powerful measure for quartet tree inference using phylogenetic identities and Markov invariants.

PubMed

Sumner, Jeremy G; Taylor, Amelia; Holland, Barbara R; Jarvis, Peter D

2017-12-01

Recently there has been renewed interest in phylogenetic inference methods based on phylogenetic invariants, alongside the related Markov invariants. Broadly speaking, both these approaches give rise to polynomial functions of sequence site patterns that, in expectation value, either vanish for particular evolutionary trees (in the case of phylogenetic invariants) or have well understood transformation properties (in the case of Markov invariants). While both approaches have been valued for their intrinsic mathematical interest, it is not clear how they relate to each other, and to what extent they can be used as practical tools for inference of phylogenetic trees. In this paper, by focusing on the special case of binary sequence data and quartets of taxa, we are able to view these two different polynomial-based approaches within a common framework. To motivate the discussion, we present three desirable statistical properties that we argue any invariant-based phylogenetic method should satisfy: (1) sensible behaviour under reordering of input sequences; (2) stability as the taxa evolve independently according to a Markov process; and (3) explicit dependence on the assumption of a continuous-time process. Motivated by these statistical properties, we develop and explore several new phylogenetic inference methods. In particular, we develop a statistically bias-corrected version of the Markov invariants approach which satisfies all three properties. We also extend previous work by showing that the phylogenetic invariants can be implemented in such a way as to satisfy property (3). A simulation study shows that, in comparison to other methods, our new proposed approach based on bias-corrected Markov invariants is extremely powerful for phylogenetic inference. The binary case is of particular theoretical interest as-in this case only-the Markov invariants can be expressed as linear combinations of the phylogenetic invariants. A wider implication of this is that, for models with more than two states-for example DNA sequence alignments with four-state models-we find that methods which rely on phylogenetic invariants are incapable of satisfying all three of the stated statistical properties. This is because in these cases the relevant Markov invariants belong to a class of polynomials independent from the phylogenetic invariants.

Availability Control for Means of Transport in Decisive Semi-Markov Models of Exploitation Process

NASA Astrophysics Data System (ADS)

Migawa, Klaudiusz

2012-12-01

The issues presented in this research paper refer to problems connected with the control process for exploitation implemented in the complex systems of exploitation for technical objects. The article presents the description of the method concerning the control availability for technical objects (means of transport) on the basis of the mathematical model of the exploitation process with the implementation of the decisive processes by semi-Markov. The presented method means focused on the preparing the decisive for the exploitation process for technical objects (semi-Markov model) and after that specifying the best control strategy (optimal strategy) from among possible decisive variants in accordance with the approved criterion (criteria) of the activity evaluation of the system of exploitation for technical objects. In the presented method specifying the optimal strategy for control availability in the technical objects means a choice of a sequence of control decisions made in individual states of modelled exploitation process for which the function being a criterion of evaluation reaches the extreme value. In order to choose the optimal control strategy the implementation of the genetic algorithm was chosen. The opinions were presented on the example of the exploitation process of the means of transport implemented in the real system of the bus municipal transport. The model of the exploitation process for the means of transports was prepared on the basis of the results implemented in the real transport system. The mathematical model of the exploitation process was built taking into consideration the fact that the model of the process constitutes the homogenous semi-Markov process.

On spatial mutation-selection models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kondratiev, Yuri, E-mail: kondrat@math.uni-bielefeld.de; Kutoviy, Oleksandr, E-mail: kutoviy@math.uni-bielefeld.de, E-mail: kutovyi@mit.edu; Department of Mathematics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139

2013-11-15

We discuss the selection procedure in the framework of mutation models. We study the regulation for stochastically developing systems based on a transformation of the initial Markov process which includes a cost functional. The transformation of initial Markov process by cost functional has an analytic realization in terms of a Kimura-Maruyama type equation for the time evolution of states or in terms of the corresponding Feynman-Kac formula on the path space. The state evolution of the system including the limiting behavior is studied for two types of mutation-selection models.

A Linear Regression and Markov Chain Model for the Arabian Horse Registry

DTIC Science & Technology

1993-04-01

as a tax deduction? Yes No T-4367 68 26. Regardless of previous equine tax deductions, do you consider your current horse activities to be... (Mark one...E L T-4367 A Linear Regression and Markov Chain Model For the Arabian Horse Registry Accesion For NTIS CRA&I UT 7 4:iC=D 5 D-IC JA" LI J:13tjlC,3 lO...the Arabian Horse Registry, which needed to forecast its future registration of purebred Arabian horses . A linear regression model was utilized to

Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

NASA Astrophysics Data System (ADS)

Nedialkova, Lilia V.; Amat, Miguel A.; Kevrekidis, Ioannis G.; Hummer, Gerhard

2014-09-01

Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space.

Covariate adjustment of event histories estimated from Markov chains: the additive approach.

PubMed

Aalen, O O; Borgan, O; Fekjaer, H

2001-12-01

Markov chain models are frequently used for studying event histories that include transitions between several states. An empirical transition matrix for nonhomogeneous Markov chains has previously been developed, including a detailed statistical theory based on counting processes and martingales. In this article, we show how to estimate transition probabilities dependent on covariates. This technique may, e.g., be used for making estimates of individual prognosis in epidemiological or clinical studies. The covariates are included through nonparametric additive models on the transition intensities of the Markov chain. The additive model allows for estimation of covariate-dependent transition intensities, and again a detailed theory exists based on counting processes. The martingale setting now allows for a very natural combination of the empirical transition matrix and the additive model, resulting in estimates that can be expressed as stochastic integrals, and hence their properties are easily evaluated. Two medical examples will be given. In the first example, we study how the lung cancer mortality of uranium miners depends on smoking and radon exposure. In the second example, we study how the probability of being in response depends on patient group and prophylactic treatment for leukemia patients who have had a bone marrow transplantation. A program in R and S-PLUS that can carry out the analyses described here has been developed and is freely available on the Internet.

Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nedialkova, Lilia V.; Amat, Miguel A.; Kevrekidis, Ioannis G., E-mail: yannis@princeton.edu, E-mail: gerhard.hummer@biophys.mpg.de

Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlapmore » with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space.« less

Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

PubMed Central

Nedialkova, Lilia V.; Amat, Miguel A.; Kevrekidis, Ioannis G.; Hummer, Gerhard

2014-01-01

Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space. PMID:25240340

Combination of Markov state models and kinetic networks for the analysis of molecular dynamics simulations of peptide folding.

PubMed

Radford, Isolde H; Fersht, Alan R; Settanni, Giovanni

2011-06-09

Atomistic molecular dynamics simulations of the TZ1 beta-hairpin peptide have been carried out using an implicit model for the solvent. The trajectories have been analyzed using a Markov state model defined on the projections along two significant observables and a kinetic network approach. The Markov state model allowed for an unbiased identification of the metastable states of the system, and provided the basis for commitment probability calculations performed on the kinetic network. The kinetic network analysis served to extract the main transition state for folding of the peptide and to validate the results from the Markov state analysis. The combination of the two techniques allowed for a consistent and concise characterization of the dynamics of the peptide. The slowest relaxation process identified is the exchange between variably folded and denatured species, and the second slowest process is the exchange between two different subsets of the denatured state which could not be otherwise identified by simple inspection of the projected trajectory. The third slowest process is the exchange between a fully native and a partially folded intermediate state characterized by a native turn with a proximal backbone H-bond, and frayed side-chain packing and termini. The transition state for the main folding reaction is similar to the intermediate state, although a more native like side-chain packing is observed.

Detection method of financial crisis in Indonesia using MSGARCH models based on banking condition indicators

NASA Astrophysics Data System (ADS)

Sugiyanto; Zukhronah, E.; Sari, S. P.

2018-05-01

Financial crisis has hit Indonesia for several times resulting the needs for an early detection system to minimize the impact. One of many methods that can be used to detect the crisis is to model the crisis indicators using combination of volatility and Markov switching models [5]. There are some indicators that can be used to detect financial crisis. Three of them are the difference between interest rate on deposit and lending, the real interest rate on deposit, and the difference between real BI rate and real Fed rate which can be referred as banking condition indicators. Volatility model used to overcome the conditional variance that change over time. Combination of volatility and Markov switching models used to detect condition change on the data. The smoothed probability from the combined models can be used to detect the crisis. This research resulted that the best combined volatility and Markov switching models for the three indicators are MS-GARCH(3,1,1) models with three states assumption. Crises in mid of 1997 until 1998 has successfully detected with a certain range of smoothed probability value for the three indicators.

Thermodynamically accurate modeling of the catalytic cycle of photosynthetic oxygen evolution: a mathematical solution to asymmetric Markov chains.

PubMed

Vinyard, David J; Zachary, Chase E; Ananyev, Gennady; Dismukes, G Charles

2013-07-01

Forty-three years ago, Kok and coworkers introduced a phenomenological model describing period-four oscillations in O2 flash yields during photosynthetic water oxidation (WOC), which had been first reported by Joliot and coworkers. The original two-parameter Kok model was subsequently extended in its level of complexity to better simulate diverse data sets, including intact cells and isolated PSII-WOCs, but at the expense of introducing physically unrealistic assumptions necessary to enable numerical solutions. To date, analytical solutions have been found only for symmetric Kok models (inefficiencies are equally probable for all intermediates, called "S-states"). However, it is widely accepted that S-state reaction steps are not identical and some are not reversible (by thermodynamic restraints) thereby causing asymmetric cycles. We have developed a mathematically more rigorous foundation that eliminates unphysical assumptions known to be in conflict with experiments and adopts a new experimental constraint on solutions. This new algorithm termed STEAMM for S-state Transition Eigenvalues of Asymmetric Markov Models enables solutions to models having fewer adjustable parameters and uses automated fitting to experimental data sets, yielding higher accuracy and precision than the classic Kok or extended Kok models. This new tool provides a general mathematical framework for analyzing damped oscillations arising from any cycle period using any appropriate Markov model, regardless of symmetry. We illustrate applications of STEAMM that better describe the intrinsic inefficiencies for photon-to-charge conversion within PSII-WOCs that are responsible for damped period-four and period-two oscillations of flash O2 yields across diverse species, while using simpler Markov models free from unrealistic assumptions. Copyright © 2013 Elsevier B.V. All rights reserved.

Can discrete event simulation be of use in modelling major depression?

PubMed Central

Le Lay, Agathe; Despiegel, Nicolas; François, Clément; Duru, Gérard

2006-01-01

Background Depression is among the major contributors to worldwide disease burden and adequate modelling requires a framework designed to depict real world disease progression as well as its economic implications as closely as possible. Objectives In light of the specific characteristics associated with depression (multiple episodes at varying intervals, impact of disease history on course of illness, sociodemographic factors), our aim was to clarify to what extent "Discrete Event Simulation" (DES) models provide methodological benefits in depicting disease evolution. Methods We conducted a comprehensive review of published Markov models in depression and identified potential limits to their methodology. A model based on DES principles was developed to investigate the benefits and drawbacks of this simulation method compared with Markov modelling techniques. Results The major drawback to Markov models is that they may not be suitable to tracking patients' disease history properly, unless the analyst defines multiple health states, which may lead to intractable situations. They are also too rigid to take into consideration multiple patient-specific sociodemographic characteristics in a single model. To do so would also require defining multiple health states which would render the analysis entirely too complex. We show that DES resolve these weaknesses and that its flexibility allow patients with differing attributes to move from one event to another in sequential order while simultaneously taking into account important risk factors such as age, gender, disease history and patients attitude towards treatment, together with any disease-related events (adverse events, suicide attempt etc.). Conclusion DES modelling appears to be an accurate, flexible and comprehensive means of depicting disease progression compared with conventional simulation methodologies. Its use in analysing recurrent and chronic diseases appears particularly useful compared with Markov processes. PMID:17147790

Can discrete event simulation be of use in modelling major depression?

PubMed

Le Lay, Agathe; Despiegel, Nicolas; François, Clément; Duru, Gérard

2006-12-05

Depression is among the major contributors to worldwide disease burden and adequate modelling requires a framework designed to depict real world disease progression as well as its economic implications as closely as possible. In light of the specific characteristics associated with depression (multiple episodes at varying intervals, impact of disease history on course of illness, sociodemographic factors), our aim was to clarify to what extent "Discrete Event Simulation" (DES) models provide methodological benefits in depicting disease evolution. We conducted a comprehensive review of published Markov models in depression and identified potential limits to their methodology. A model based on DES principles was developed to investigate the benefits and drawbacks of this simulation method compared with Markov modelling techniques. The major drawback to Markov models is that they may not be suitable to tracking patients' disease history properly, unless the analyst defines multiple health states, which may lead to intractable situations. They are also too rigid to take into consideration multiple patient-specific sociodemographic characteristics in a single model. To do so would also require defining multiple health states which would render the analysis entirely too complex. We show that DES resolve these weaknesses and that its flexibility allow patients with differing attributes to move from one event to another in sequential order while simultaneously taking into account important risk factors such as age, gender, disease history and patients attitude towards treatment, together with any disease-related events (adverse events, suicide attempt etc.). DES modelling appears to be an accurate, flexible and comprehensive means of depicting disease progression compared with conventional simulation methodologies. Its use in analysing recurrent and chronic diseases appears particularly useful compared with Markov processes.

Cost-Effectiveness of Blood Donation Screening for Trypanosoma cruzi in Mexico.

PubMed

Sánchez-González, Gilberto; Figueroa-Lara, Alejandro; Elizondo-Cano, Miguel; Wilson, Leslie; Novelo-Garza, Barbara; Valiente-Banuet, Leopoldo; Ramsey, Janine M

2016-03-01

An estimated 2 million inhabitants are infected with Chagas disease in Mexico, with highest prevalence coinciding with highest demographic density in the southern half of the country. After vector-borne transmission, Trypanosoma cruzi is principally transmitted to humans via blood transfusion. Despite initiation of serological screening of blood donations or donors for T. cruzi since 1990 in most Latin American countries, Mexico only finally included mandatory serological screening nationwide in official Norms in 2012. Most recent regulatory changes and segmented blood services in Mexico may affect compliance of mandatory screening guidelines. The objective of this study was to calculate the incremental cost-effectiveness ratio for total compliance of current guidelines from both Mexican primary healthcare and regular salaried worker health service institutions: the Secretary of Health and the Mexican Institute for Social Security. We developed a bi-modular model to analyze compliance using a decision tree for the most common screening algorithms for each health institution, and a Markov transition model for the natural history of illness and care. The incremental cost effectiveness ratio based on life-years gained is US$ 383 for the Secretary of Health, while the cost for an additional life-year gained is US$ 463 for the Social Security Institute. The results of the present study suggest that due to incomplete compliance of Mexico's national legislation during 2013 and 2014, the MoH has failed to confirm 15,162 T. cruzi infections, has not prevented 2,347 avoidable infections, and has lost 333,483 life-years. Although there is a vast difference in T. cruzi prevalence between Bolivia and Mexico, Bolivia established mandatory blood screening for T.cruzi in 1996 and until 2002 detected and discarded 11,489 T. cruzi -infected blood units and prevented 2,879 potential infections with their transfusion blood screening program. In the first two years of Mexico's mandated program, the two primary institutions failed to prevent due to incomplete compliance more potential infections than those gained from the first five years of Bolivia's program. Full regulatory compliance should be clearly understood as mandatory for the sake of blood security, and its monitoring and analysis in Mexico should be part of the health authority's responsibility.

True and apparent scaling: The proximity of the Markov-switching multifractal model to long-range dependence

NASA Astrophysics Data System (ADS)

Liu, Ruipeng; Di Matteo, T.; Lux, Thomas

2007-09-01

In this paper, we consider daily financial data of a collection of different stock market indices, exchange rates, and interest rates, and we analyze their multi-scaling properties by estimating a simple specification of the Markov-switching multifractal (MSM) model. In order to see how well the estimated model captures the temporal dependence of the data, we estimate and compare the scaling exponents H(q) (for q=1,2) for both empirical data and simulated data of the MSM model. In most cases the multifractal model appears to generate ‘apparent’ long memory in agreement with the empirical scaling laws.

Hidden Markov models of biological primary sequence information.

PubMed Central

Baldi, P; Chauvin, Y; Hunkapiller, T; McClure, M A

1994-01-01

Hidden Markov model (HMM) techniques are used to model families of biological sequences. A smooth and convergent algorithm is introduced to iteratively adapt the transition and emission parameters of the models from the examples in a given family. The HMM approach is applied to three protein families: globins, immunoglobulins, and kinases. In all cases, the models derived capture the important statistical characteristics of the family and can be used for a number of tasks, including multiple alignments, motif detection, and classification. For K sequences of average length N, this approach yields an effective multiple-alignment algorithm which requires O(KN2) operations, linear in the number of sequences. PMID:8302831

A Markov chain model for reliability growth and decay

NASA Technical Reports Server (NTRS)

Siegrist, K.

1982-01-01

A mathematical model is developed to describe a complex system undergoing a sequence of trials in which there is interaction between the internal states of the system and the outcomes of the trials. For example, the model might describe a system undergoing testing that is redesigned after each failure. The basic assumptions for the model are that the state of the system after a trial depends probabilistically only on the state before the trial and on the outcome of the trial and that the outcome of a trial depends probabilistically only on the state of the system before the trial. It is shown that under these basic assumptions, the successive states form a Markov chain and the successive states and outcomes jointly form a Markov chain. General results are obtained for the transition probabilities, steady-state distributions, etc. A special case studied in detail describes a system that has two possible state ('repaired' and 'unrepaired') undergoing trials that have three possible outcomes ('inherent failure', 'assignable-cause' 'failure' and 'success'). For this model, the reliability function is computed explicitly and an optimal repair policy is obtained.

Using hidden Markov models to align multiple sequences.

PubMed

Mount, David W

2009-07-01

A hidden Markov model (HMM) is a probabilistic model of a multiple sequence alignment (msa) of proteins. In the model, each column of symbols in the alignment is represented by a frequency distribution of the symbols (called a "state"), and insertions and deletions are represented by other states. One moves through the model along a particular path from state to state in a Markov chain (i.e., random choice of next move), trying to match a given sequence. The next matching symbol is chosen from each state, recording its probability (frequency) and also the probability of going to that state from a previous one (the transition probability). State and transition probabilities are multiplied to obtain a probability of the given sequence. The hidden nature of the HMM is due to the lack of information about the value of a specific state, which is instead represented by a probability distribution over all possible values. This article discusses the advantages and disadvantages of HMMs in msa and presents algorithms for calculating an HMM and the conditions for producing the best HMM.

Hidden Markov models and neural networks for fault detection in dynamic systems

NASA Technical Reports Server (NTRS)

Smyth, Padhraic

1994-01-01

Neural networks plus hidden Markov models (HMM) can provide excellent detection and false alarm rate performance in fault detection applications, as shown in this viewgraph presentation. Modified models allow for novelty detection. Key contributions of neural network models are: (1) excellent nonparametric discrimination capability; (2) a good estimator of posterior state probabilities, even in high dimensions, and thus can be embedded within overall probabilistic model (HMM); and (3) simple to implement compared to other nonparametric models. Neural network/HMM monitoring model is currently being integrated with the new Deep Space Network (DSN) antenna controller software and will be on-line monitoring a new DSN 34-m antenna (DSS-24) by July, 1994.

Cost-effectiveness analysis of different types of human papillomavirus vaccination combined with a cervical cancer screening program in mainland China.

PubMed

Mo, Xiuting; Gai Tobe, Ruoyan; Wang, Lijie; Liu, Xianchen; Wu, Bin; Luo, Huiwen; Nagata, Chie; Mori, Rintaro; Nakayama, Takeo

2017-07-18

China has a high prevalence of human papillomavirus (HPV) and a consequently high burden of disease with respect to cervical cancer. The HPV vaccine has proved to be effective in preventing cervical cancer and is now a part of routine immunization programs worldwide. It has also proved to be cost effective. This study aimed to assess the cost-effectiveness of 2-, 4-, and 9-valent HPV vaccines (hereafter, HPV2, 4 or 9) combined with current screening strategies in China. A Markov model was developed for a cohort of 100,000 HPV-free girls to simulate the natural history to HPV infection. Three recommended screening methods (1. liquid-based cytology test + HPV DNA test; 2. pap smear cytology test + HPV DNA test; 3. visual inspection with acetic acid) and three types of HPV vaccination program (HPV2/4/9) were incorporated into 15 intervention options, and the incremental cost-effectiveness ratio (ICER) was calculated to determine the dominant strategies. Costs, transition probabilities and utilities were obtained from a review of the literature and national databases. One-way sensitivity analyses and threshold analyses were performed for key variables in different vaccination scenarios. HPV9 combined with screening showed the highest health impact in terms of reducing HPV-related diseases and increasing the number of quality-adjusted life years (QALYs). Under the current thresholds of willingness to pay (WTP, 3 times the per capita GDP or USD$ 23,880), HPV4/9 proved highly cost effective, while HPV2 combined with screening cost more and was less cost effective. Only when screening coverage increased to 60% ~ 70% did the HPV2 and screening combination strategy become economically feasible. The combination of the HPV4/9 vaccine with current screening strategies for adolescent girls was highly cost-effective and had a significant impact on reducing the HPV infection-related disease burden in Mainland China.

Cost-effective analysis of screening for biliary atresia with the stool color card.

PubMed

Mogul, Douglas; Zhou, Mo; Intihar, Paul; Schwarz, Kathleen; Frick, Kevin

2015-01-01

Biliary atresia (BA) is the leading cause of pediatric end-stage liver disease and liver transplantation in the United States. Early diagnosis leads to improved outcomes, but diagnosis is often delayed, leading to increased rates of transplantation and mortality. A Markov model was developed to simulate the natural history and transplant-related outcomes of patients with BA in a US cohort studied for 20 years. Data regarding proportions of individuals in different health states, including transplant and death, were obtained from published literature. Costs were derived from the literature and the Johns Hopkins database of charges using the cost-to-charge ratio. Strategy A represented the status quo and assumed no screening. Strategy B used nationwide screening with the stool color card developed by the Taiwan Health Bureau. The cost associated with both strategies was compared with the number of life-years gained, deaths, and the number of transplants for a 20-year interval. A dominant strategy was one that was associated with lower cost alongside improved outcomes, including increases in life-years gained, reductions in number of deaths, and reductions in number of transplants. One-way and probabilistic sensitivity analyses were performed. In strategy A, the 20-year cost was $142,479,725 with 3702 life-years, 74 deaths and 158 liver transplants. For strategy B, the cost was $133,893,563 with 3731.7 life-years, 71 deaths and 147 liver transplants. There was a >97% probability that screening with the stool color card would be cost saving and associated with an increase in life-years gained. Among all parameters, only stool color card specificity was associated with the potential for screening to no longer be cost saving. Compared with no screening, screening with the stool color card is a dominant strategy associated with lower costs and better outcomes. These findings suggest that screening with the stool color card could be an important, economically feasible strategy for improving outcomes in BA in the United States.

Advanced techniques in reliability model representation and solution

NASA Technical Reports Server (NTRS)

Palumbo, Daniel L.; Nicol, David M.

1992-01-01

The current tendency of flight control system designs is towards increased integration of applications and increased distribution of computational elements. The reliability analysis of such systems is difficult because subsystem interactions are increasingly interdependent. Researchers at NASA Langley Research Center have been working for several years to extend the capability of Markov modeling techniques to address these problems. This effort has been focused in the areas of increased model abstraction and increased computational capability. The reliability model generator (RMG) is a software tool that uses as input a graphical object-oriented block diagram of the system. RMG uses a failure-effects algorithm to produce the reliability model from the graphical description. The ASSURE software tool is a parallel processing program that uses the semi-Markov unreliability range evaluator (SURE) solution technique and the abstract semi-Markov specification interface to the SURE tool (ASSIST) modeling language. A failure modes-effects simulation is used by ASSURE. These tools were used to analyze a significant portion of a complex flight control system. The successful combination of the power of graphical representation, automated model generation, and parallel computation leads to the conclusion that distributed fault-tolerant system architectures can now be analyzed.

Experiences with Markov Chain Monte Carlo Convergence Assessment in Two Psychometric Examples

ERIC Educational Resources Information Center

Sinharay, Sandip

2004-01-01

There is an increasing use of Markov chain Monte Carlo (MCMC) algorithms for fitting statistical models in psychometrics, especially in situations where the traditional estimation techniques are very difficult to apply. One of the disadvantages of using an MCMC algorithm is that it is not straightforward to determine the convergence of the…

Chutes and Ladders for the Impatient

ERIC Educational Resources Information Center

Cheteyan, Leslie A.; Hengeveld, Stewart; Jones, Michael A.

2011-01-01

In this paper, we review the rules and game board for "Chutes and Ladders", define a Markov chain to model the game regardless of the spinner range, and describe how properties of Markov chains are used to determine that an optimal spinner range of 15 minimizes the expected number of turns for a player to complete the game. Because the Markov…

Students' Progress throughout Examination Process as a Markov Chain

ERIC Educational Resources Information Center

Hlavatý, Robert; Dömeová, Ludmila

2014-01-01

The paper is focused on students of Mathematical methods in economics at the Czech university of life sciences (CULS) in Prague. The idea is to create a model of students' progress throughout the whole course using the Markov chain approach. Each student has to go through various stages of the course requirements where his success depends on the…

Hidden Markov models for evolution and comparative genomics analysis.

PubMed

Bykova, Nadezda A; Favorov, Alexander V; Mironov, Andrey A

2013-01-01

The problem of reconstruction of ancestral states given a phylogeny and data from extant species arises in a wide range of biological studies. The continuous-time Markov model for the discrete states evolution is generally used for the reconstruction of ancestral states. We modify this model to account for a case when the states of the extant species are uncertain. This situation appears, for example, if the states for extant species are predicted by some program and thus are known only with some level of reliability; it is common for bioinformatics field. The main idea is formulation of the problem as a hidden Markov model on a tree (tree HMM, tHMM), where the basic continuous-time Markov model is expanded with the introduction of emission probabilities of observed data (e.g. prediction scores) for each underlying discrete state. Our tHMM decoding algorithm allows us to predict states at the ancestral nodes as well as to refine states at the leaves on the basis of quantitative comparative genomics. The test on the simulated data shows that the tHMM approach applied to the continuous variable reflecting the probabilities of the states (i.e. prediction score) appears to be more accurate then the reconstruction from the discrete states assignment defined by the best score threshold. We provide examples of applying our model to the evolutionary analysis of N-terminal signal peptides and transcription factor binding sites in bacteria. The program is freely available at http://bioinf.fbb.msu.ru/~nadya/tHMM and via web-service at http://bioinf.fbb.msu.ru/treehmmweb.

Sensitivity Study for Long Term Reliability

NASA Technical Reports Server (NTRS)

White, Allan L.

2008-01-01

This paper illustrates using Markov models to establish system and maintenance requirements for small electronic controllers where the goal is a high probability of continuous service for a long period of time. The system and maintenance items considered are quality of components, various degrees of simple redundancy, redundancy with reconfiguration, diagnostic levels, periodic maintenance, and preventive maintenance. Markov models permit a quantitative investigation with comparison and contrast. An element of special interest is the use of conditional probability to study the combination of imperfect diagnostics and periodic maintenance.

Power spectral ensity of markov texture fields

NASA Technical Reports Server (NTRS)

Shanmugan, K. S.; Holtzman, J. C.

1984-01-01

Texture is an important image characteristic. A variety of spatial domain techniques were proposed for extracting and utilizing textural features for segmenting and classifying images. for the most part, these spatial domain techniques are ad hos in nature. A markov random field model for image texture is discussed. A frequency domain description of image texture is derived in terms of the power spectral density. This model is used for designing optimum frequency domain filters for enhancing, restoring and segmenting images based on their textural properties.

Integration within the Felsenstein equation for improved Markov chain Monte Carlo methods in population genetics

PubMed Central

Hey, Jody; Nielsen, Rasmus

2007-01-01

In 1988, Felsenstein described a framework for assessing the likelihood of a genetic data set in which all of the possible genealogical histories of the data are considered, each in proportion to their probability. Although not analytically solvable, several approaches, including Markov chain Monte Carlo methods, have been developed to find approximate solutions. Here, we describe an approach in which Markov chain Monte Carlo simulations are used to integrate over the space of genealogies, whereas other parameters are integrated out analytically. The result is an approximation to the full joint posterior density of the model parameters. For many purposes, this function can be treated as a likelihood, thereby permitting likelihood-based analyses, including likelihood ratio tests of nested models. Several examples, including an application to the divergence of chimpanzee subspecies, are provided. PMID:17301231

Metastates in Mean-Field Models with Random External Fields Generated by Markov Chains

NASA Astrophysics Data System (ADS)

Formentin, M.; Külske, C.; Reichenbachs, A.

2012-01-01

We extend the construction by Külske and Iacobelli of metastates in finite-state mean-field models in independent disorder to situations where the local disorder terms are a sample of an external ergodic Markov chain in equilibrium. We show that for non-degenerate Markov chains, the structure of the theorems is analogous to the case of i.i.d. variables when the limiting weights in the metastate are expressed with the aid of a CLT for the occupation time measure of the chain. As a new phenomenon we also show in a Potts example that for a degenerate non-reversible chain this CLT approximation is not enough, and that the metastate can have less symmetry than the symmetry of the interaction and a Gaussian approximation of disorder fluctuations would suggest.

Cost effectiveness of interferon-gamma release assay for tuberculosis screening using three months of rifapentine and isoniazid among long-term expatriates from low to high incidence countries.

PubMed

Kowada, Akiko

Long-term expatriates from low to high tuberculosis (TB) incidence countries get high rates of active TB and latent TB infection (LTBI). TB screening for expatriates is important for occupational health. Interferon-gamma release assays are more accurate than tuberculin skin test (TST). Rifapentine plus isoniazid for 3 months (3HP) is as effective as 9 months of isoniazid (9H) with a higher treatment-completion rate. Decision trees and Markov models were constructed using a societal perspective on a lifetime horizon. The target population was a hypothetical cohort of 30 year-old expatriates. Seven strategies; TST with 3HP or 9H, QuantiFERON ® -TB Gold In-Tube (QFT) with 3HP or 9H, T-SPOT ® .TB (TSPOT) with 3HP or 9H and chest X-ray examination (CXR) were modeled. The main outcome measure of effectiveness was quality-adjusted life-years (QALYs) gained. QFT with 3HP yielded the greatest benefits with the lowest cost ($US 674.8; 25.95660 QALYs [year 2012 values]). CXR was the least cost-effective ($US 13,666.8; 24.62917 QALYs). Cost-effectiveness was sensitive to adherence rate of 3HP and QFT specificity, but not to BCG vaccination rate. Entry LTBI screening using QFT treated with 3HP is recommended on the basis of cost effectiveness among long-term expatriates from low to high incidence countries. Copyright © 2016 Elsevier Ltd. All rights reserved.

Value of lifestyle intervention to prevent diabetes and sequelae.

PubMed

Dall, Timothy M; Storm, Michael V; Semilla, April P; Wintfeld, Neil; O'Grady, Michael; Narayan, K M Venkat

2015-03-01

The Community Preventive Services Task Force recommends combined diet and physical activity promotion programs for people at increased risk of type 2 diabetes, as evidence continues to show that intensive lifestyle interventions are effective for overweight individuals with prediabetes. To illustrate the potential clinical and economic benefits of treating prediabetes with lifestyle intervention to prevent or delay onset of type 2 diabetes and sequelae. This 2014 analysis used a Markov model to simulate disease onset, medical expenditures, economic outcomes, mortality, and quality of life for a nationally representative sample with prediabetes from the 2003-2010 National Health and Nutrition Examination Survey. Modeled scenarios used 10-year follow-up results from the lifestyle arm of the Diabetes Prevention Program and Outcomes Study versus simulated natural history of disease. Over 10 years, estimated average cumulative gross economic benefits of treating patients who met diabetes screening criteria recommended by the ADA ($26,800) or USPSTF ($24,700) exceeded average benefits from treating the entire prediabetes population ($17,800). Estimated cumulative, gross medical savings for these three populations averaged $10,400, $11,200, and $6,300, respectively. Published estimates suggest that opportunistic screening for prediabetes is inexpensive, and lifestyle intervention similar to the Diabetes Prevention Program can be achieved for ≤$2,300 over 10 years. Lifestyle intervention among people with prediabetes produces long-term societal benefits that exceed anticipated intervention costs, especially among prediabetes patients that meet the ADA and USPSTF screening guidelines. Copyright © 2015 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Parametric inference for biological sequence analysis.

PubMed

Pachter, Lior; Sturmfels, Bernd

2004-11-16

One of the major successes in computational biology has been the unification, by using the graphical model formalism, of a multitude of algorithms for annotating and comparing biological sequences. Graphical models that have been applied to these problems include hidden Markov models for annotation, tree models for phylogenetics, and pair hidden Markov models for alignment. A single algorithm, the sum-product algorithm, solves many of the inference problems that are associated with different statistical models. This article introduces the polytope propagation algorithm for computing the Newton polytope of an observation from a graphical model. This algorithm is a geometric version of the sum-product algorithm and is used to analyze the parametric behavior of maximum a posteriori inference calculations for graphical models.

Model-Averaged ℓ1 Regularization using Markov Chain Monte Carlo Model Composition

PubMed Central

Fraley, Chris; Percival, Daniel

2014-01-01

Bayesian Model Averaging (BMA) is an effective technique for addressing model uncertainty in variable selection problems. However, current BMA approaches have computational difficulty dealing with data in which there are many more measurements (variables) than samples. This paper presents a method for combining ℓ1 regularization and Markov chain Monte Carlo model composition techniques for BMA. By treating the ℓ1 regularization path as a model space, we propose a method to resolve the model uncertainty issues arising in model averaging from solution path point selection. We show that this method is computationally and empirically effective for regression and classification in high-dimensional datasets. We apply our technique in simulations, as well as to some applications that arise in genomics. PMID:25642001

Hidden Markov model analysis of force/torque information in telemanipulation

NASA Technical Reports Server (NTRS)

Hannaford, Blake; Lee, Paul

1991-01-01

A model for the prediction and analysis of sensor information recorded during robotic performance of telemanipulation tasks is presented. The model uses the hidden Markov model to describe the task structure, the operator's or intelligent controller's goal structure, and the sensor signals. A methodology for constructing the model parameters based on engineering knowledge of the task is described. It is concluded that the model and its optimal state estimation algorithm, the Viterbi algorithm, are very succesful at the task of segmenting the data record into phases corresponding to subgoals of the task. The model provides a rich modeling structure within a statistical framework, which enables it to represent complex systems and be robust to real-world sensory signals.

Determining the cost-effectiveness of endoscopic surveillance for gastric cancer in patients with precancerous lesions.

PubMed

Wu, Jin Tong; Zhou, Jun; Naidoo, Nasheen; Yang, Wen Yu; Lin, Xiao Cheng; Wang, Pei; Ding, Jin Qin; Wu, Chen Bin; Zhou, Hui Jun

2016-12-01

To identify the optimal strategy for gastric cancer (GC) prevention by evaluating the cost-effectiveness of esophagogastroduodenoscopy (EGD)-based preventive strategies. We conducted a model-based cost-effectiveness analysis. Adopting a healthcare payer's perspective, Markov models simulated the clinical experience of the target population (Singaporean Chinese 50-69 years old) undergoing endoscopic screening, endoscopic surveillance and usual care of do-nothing. The screening strategy examined the cohort every alternate year whereas the surveillance strategy provided annual EGD only to people with precancerous lesions. For each strategy, discounted lifetime costs ($) and quality adjusted life years (QALY) were estimated and compared to generate incremental cost-effectiveness ratio (ICER). Deterministic and probabilistic sensitivity analysis was conducted to identify influential parameters and quantify the impact of model uncertainties. Annual EGD surveillance with an ICER of $34 200/QALY was deemed cost-effective for GC prevention within the Singapore healthcare system. To inform implementation, the models identified six influential factors and their respective thresholds, namely discount rate (<4.20%), age of starting surveillance (>51.6 years), proportion of program cost in delivering endoscopy (<65%), cost of follow-up EGD (<$484), utility of stage 1 GC patients (>0.72) and odds ratio of GC for high-risk subjects (>3.93). The likelihood that surveillance is the most cost-effective strategy is 69.5% accounting for model uncertainties. Endoscopic surveillance of gastric premalignancies can be a cost-effective strategy for GC prevention. Its implementation requires careful assessment on factors influencing the actual cost-effectiveness. © 2016 John Wiley & Sons Australia, Ltd.

Frequency of Evidence-Based Screening for Retinopathy in Type 1 Diabetes.

PubMed

Nathan, David M; Bebu, Ionut; Hainsworth, Dean; Klein, Ronald; Tamborlane, William; Lorenzi, Gayle; Gubitosi-Klug, Rose; Lachin, John M

2017-04-20

In patients who have had type 1 diabetes for 5 years, current recommendations regarding screening for diabetic retinopathy include annual dilated retinal examinations to detect proliferative retinopathy or clinically significant macular edema, both of which require timely intervention to preserve vision. During 30 years of the Diabetes Control and Complications Trial (DCCT) and its longitudinal follow-up Epidemiology of Diabetes Interventions and Complications (EDIC) study, retinal photography was performed at intervals of 6 months to 4 years. We used retinal photographs from the DCCT/EDIC study to develop a rational screening frequency for retinopathy. Markov modeling was used to determine the likelihood of progression to proliferative diabetic retinopathy or clinically significant macular edema in patients with various initial retinopathy levels (no retinopathy or mild, moderate, or severe nonproliferative diabetic retinopathy). The models included recognized risk factors for progression of retinopathy. Overall, the probability of progression to proliferative diabetic retinopathy or clinically significant macular edema was limited to approximately 5% between retinal screening examinations at 4 years among patients who had no retinopathy, 3 years among those with mild retinopathy, 6 months among those with moderate retinopathy, and 3 months among those with severe nonproliferative diabetic retinopathy. The risk of progression was also closely related to mean glycated hemoglobin levels. The risk of progression from no retinopathy to proliferative diabetic retinopathy or clinically significant macular edema was 1.0% over 5 years among patients with a glycated hemoglobin level of 6%, as compared with 4.3% over 3 years among patients with a glycated hemoglobin level of 10%. Over a 20-year period, the frequency of eye examinations was 58% lower with our practical, evidence-based schedule than with routine annual examinations, which resulted in substantial cost savings. Our model for establishing an individualized schedule for retinopathy screening on the basis of the patient's current state of retinopathy and glycated hemoglobin level reduced the frequency of eye examinations without delaying the diagnosis of clinically significant disease. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases and others; DCCT/EDIC ClinicalTrials.gov numbers, NCT00360893 and NCT00360815 .).

A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields

DOE PAGES

Nosedal-Sanchez, Alvaro; Jackson, Charles S.; Huerta, Gabriel

2016-07-20

A new test statistic for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new statistic uses Gaussian Markov random fields for estimating field and space dependencies within a first-order grid point neighborhood structure. We illustrate the ability of Gaussian Markov random fields to represent empirical estimates of fieldmore » and space covariances using "witch hat" graphs. We further use the new statistic to evaluate the tropical response of a climate model (CAM3.1) to changes in two parameters important to its representation of cloud and precipitation physics. Overall, the inclusion of dependency information did not alter significantly the recognition of those regions of parameter space that best approximated observations. However, there were some qualitative differences in the shape of the response surface that suggest how such a measure could affect estimates of model uncertainty.« less

A hybrid degradation tendency measurement method for mechanical equipment based on moving window and Grey-Markov model

NASA Astrophysics Data System (ADS)

Jiang, Wei; Zhou, Jianzhong; Zheng, Yang; Liu, Han

2017-11-01

Accurate degradation tendency measurement is vital for the secure operation of mechanical equipment. However, the existing techniques and methodologies for degradation measurement still face challenges, such as lack of appropriate degradation indicator, insufficient accuracy, and poor capability to track the data fluctuation. To solve these problems, a hybrid degradation tendency measurement method for mechanical equipment based on a moving window and Grey-Markov model is proposed in this paper. In the proposed method, a 1D normalized degradation index based on multi-feature fusion is designed to assess the extent of degradation. Subsequently, the moving window algorithm is integrated with the Grey-Markov model for the dynamic update of the model. Two key parameters, namely the step size and the number of states, contribute to the adaptive modeling and multi-step prediction. Finally, three types of combination prediction models are established to measure the degradation trend of equipment. The effectiveness of the proposed method is validated with a case study on the health monitoring of turbine engines. Experimental results show that the proposed method has better performance, in terms of both measuring accuracy and data fluctuation tracing, in comparison with other conventional methods.

Reliability modelling and analysis of a multi-state element based on a dynamic Bayesian network

NASA Astrophysics Data System (ADS)

Li, Zhiqiang; Xu, Tingxue; Gu, Junyuan; Dong, Qi; Fu, Linyu

2018-04-01

This paper presents a quantitative reliability modelling and analysis method for multi-state elements based on a combination of the Markov process and a dynamic Bayesian network (DBN), taking perfect repair, imperfect repair and condition-based maintenance (CBM) into consideration. The Markov models of elements without repair and under CBM are established, and an absorbing set is introduced to determine the reliability of the repairable element. According to the state-transition relations between the states determined by the Markov process, a DBN model is built. In addition, its parameters for series and parallel systems, namely, conditional probability tables, can be calculated by referring to the conditional degradation probabilities. Finally, the power of a control unit in a failure model is used as an example. A dynamic fault tree (DFT) is translated into a Bayesian network model, and subsequently extended to a DBN. The results show the state probabilities of an element and the system without repair, with perfect and imperfect repair, and under CBM, with an absorbing set plotted by differential equations and verified. Through referring forward, the reliability value of the control unit is determined in different kinds of modes. Finally, weak nodes are noted in the control unit.

Poisson-Gaussian Noise Reduction Using the Hidden Markov Model in Contourlet Domain for Fluorescence Microscopy Images

PubMed Central

Yang, Sejung; Lee, Byung-Uk

2015-01-01

In certain image acquisitions processes, like in fluorescence microscopy or astronomy, only a limited number of photons can be collected due to various physical constraints. The resulting images suffer from signal dependent noise, which can be modeled as a Poisson distribution, and a low signal-to-noise ratio. However, the majority of research on noise reduction algorithms focuses on signal independent Gaussian noise. In this paper, we model noise as a combination of Poisson and Gaussian probability distributions to construct a more accurate model and adopt the contourlet transform which provides a sparse representation of the directional components in images. We also apply hidden Markov models with a framework that neatly describes the spatial and interscale dependencies which are the properties of transformation coefficients of natural images. In this paper, an effective denoising algorithm for Poisson-Gaussian noise is proposed using the contourlet transform, hidden Markov models and noise estimation in the transform domain. We supplement the algorithm by cycle spinning and Wiener filtering for further improvements. We finally show experimental results with simulations and fluorescence microscopy images which demonstrate the improved performance of the proposed approach. PMID:26352138

A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nosedal-Sanchez, Alvaro; Jackson, Charles S.; Huerta, Gabriel

A new test statistic for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new statistic uses Gaussian Markov random fields for estimating field and space dependencies within a first-order grid point neighborhood structure. We illustrate the ability of Gaussian Markov random fields to represent empirical estimates of fieldmore » and space covariances using "witch hat" graphs. We further use the new statistic to evaluate the tropical response of a climate model (CAM3.1) to changes in two parameters important to its representation of cloud and precipitation physics. Overall, the inclusion of dependency information did not alter significantly the recognition of those regions of parameter space that best approximated observations. However, there were some qualitative differences in the shape of the response surface that suggest how such a measure could affect estimates of model uncertainty.« less

Comparison of statistical algorithms for detecting homogeneous river reaches along a longitudinal continuum

NASA Astrophysics Data System (ADS)

Leviandier, Thierry; Alber, A.; Le Ber, F.; Piégay, H.

2012-02-01

Seven methods designed to delineate homogeneous river segments, belonging to four families, namely — tests of homogeneity, contrast enhancing, spatially constrained classification, and hidden Markov models — are compared, firstly on their principles, then on a case study, and on theoretical templates. These templates contain patterns found in the case study but not considered in the standard assumptions of statistical methods, such as gradients and curvilinear structures. The influence of data resolution, noise and weak satisfaction of the assumptions underlying the methods is investigated. The control of the number of reaches obtained in order to achieve meaningful comparisons is discussed. No method is found that outperforms all the others on all trials. However, the methods with sequential algorithms (keeping at order n + 1 all breakpoints found at order n) fail more often than those running complete optimisation at any order. The Hubert-Kehagias method and Hidden Markov Models are the most successful at identifying subpatterns encapsulated within the templates. Ergodic Hidden Markov Models are, moreover, liable to exhibit transition areas.

Semi-Markov models for interval censored transient cognitive states with back transitions and a competing risk

PubMed Central

Wei, Shaoceng; Kryscio, Richard J.

2015-01-01

Continuous-time multi-state stochastic processes are useful for modeling the flow of subjects from intact cognition to dementia with mild cognitive impairment and global impairment as intervening transient, cognitive states and death as a competing risk (Figure 1). Each subject's cognition is assessed periodically resulting in interval censoring for the cognitive states while death without dementia is not interval censored. Since back transitions among the transient states are possible, Markov chains are often applied to this type of panel data. In this manuscript we apply a Semi-Markov process in which we assume that the waiting times are Weibull distributed except for transitions from the baseline state, which are exponentially distributed and in which we assume no additional changes in cognition occur between two assessments. We implement a quasi-Monte Carlo (QMC) method to calculate the higher order integration needed for likelihood estimation. We apply our model to a real dataset, the Nun Study, a cohort of 461 participants. PMID:24821001

Semi-Markov models for interval censored transient cognitive states with back transitions and a competing risk.

PubMed

Wei, Shaoceng; Kryscio, Richard J

2016-12-01

Continuous-time multi-state stochastic processes are useful for modeling the flow of subjects from intact cognition to dementia with mild cognitive impairment and global impairment as intervening transient cognitive states and death as a competing risk. Each subject's cognition is assessed periodically resulting in interval censoring for the cognitive states while death without dementia is not interval censored. Since back transitions among the transient states are possible, Markov chains are often applied to this type of panel data. In this manuscript, we apply a semi-Markov process in which we assume that the waiting times are Weibull distributed except for transitions from the baseline state, which are exponentially distributed and in which we assume no additional changes in cognition occur between two assessments. We implement a quasi-Monte Carlo (QMC) method to calculate the higher order integration needed for likelihood estimation. We apply our model to a real dataset, the Nun Study, a cohort of 461 participants. © The Author(s) 2014.

Self-Organizing Hidden Markov Model Map (SOHMMM).

PubMed

Ferles, Christos; Stafylopatis, Andreas

2013-12-01

A hybrid approach combining the Self-Organizing Map (SOM) and the Hidden Markov Model (HMM) is presented. The Self-Organizing Hidden Markov Model Map (SOHMMM) establishes a cross-section between the theoretic foundations and algorithmic realizations of its constituents. The respective architectures and learning methodologies are fused in an attempt to meet the increasing requirements imposed by the properties of deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and protein chain molecules. The fusion and synergy of the SOM unsupervised training and the HMM dynamic programming algorithms bring forth a novel on-line gradient descent unsupervised learning algorithm, which is fully integrated into the SOHMMM. Since the SOHMMM carries out probabilistic sequence analysis with little or no prior knowledge, it can have a variety of applications in clustering, dimensionality reduction and visualization of large-scale sequence spaces, and also, in sequence discrimination, search and classification. Two series of experiments based on artificial sequence data and splice junction gene sequences demonstrate the SOHMMM's characteristics and capabilities. Copyright © 2013 Elsevier Ltd. All rights reserved.

Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain

PubMed Central

Dai, Yonghui; Han, Dongmei; Dai, Weihui

2014-01-01

The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market. PMID:24782659

High-Resolution Remote Sensing Image Building Extraction Based on Markov Model

NASA Astrophysics Data System (ADS)

Zhao, W.; Yan, L.; Chang, Y.; Gong, L.

2018-04-01

With the increase of resolution, remote sensing images have the characteristics of increased information load, increased noise, more complex feature geometry and texture information, which makes the extraction of building information more difficult. To solve this problem, this paper designs a high resolution remote sensing image building extraction method based on Markov model. This method introduces Contourlet domain map clustering and Markov model, captures and enhances the contour and texture information of high-resolution remote sensing image features in multiple directions, and further designs the spectral feature index that can characterize "pseudo-buildings" in the building area. Through the multi-scale segmentation and extraction of image features, the fine extraction from the building area to the building is realized. Experiments show that this method can restrain the noise of high-resolution remote sensing images, reduce the interference of non-target ground texture information, and remove the shadow, vegetation and other pseudo-building information, compared with the traditional pixel-level image information extraction, better performance in building extraction precision, accuracy and completeness.

Cost-effectiveness analysis of prophylactic cervical cancer vaccination in Japanese women.

PubMed

Konno, Ryo; Sasagawa, Toshiyuki; Fukuda, Takashi; Van Kriekinge, Georges; Demarteau, Nadia

2010-04-01

The incidence of cervical cancer (CC) is high in Japan and is further increasing among women younger than 30 years. This burden could be reduced by the implementation of a CC vaccine, but its cost-effectiveness is unknown. We quantified the clinical impact and assessed the cost-effectiveness of adding CC vaccination at age 12 to the current screening in place in Japan with a lifetime Markov model adapted to the Japanese setting. Transition probabilities and utility values were obtained from public databases. Direct costs for treatment and screening were estimated using Japanese medical fees. Annual costs and benefits were discounted at 3%. Sensitivity analyses were conducted on the age at vaccination, the vaccine characteristics, the discount rates, the proportion of human papillomavirus types 16/18 in cancer, and the screening coverage. Vaccinating a 12-year-old cohort was predicted to reduce CC incidence and deaths from CC by 73%. These clinical effects were associated with an incremental cost-effectiveness ratio of yen1.8 million per quality-adjusted life year gained. The incremental cost-effectiveness ratio of vaccinating all 10- to 45-year-old women was yen2.8 million per quality-adjusted life year, still below the threshold value. The implementation of a CC vaccination in Japan could reduce the CC burden in a very cost-effective manner for women up to 45 years.

Stochastic-shielding approximation of Markov chains and its application to efficiently simulate random ion-channel gating.

PubMed

Schmandt, Nicolaus T; Galán, Roberto F

2012-09-14

Markov chains provide realistic models of numerous stochastic processes in nature. We demonstrate that in any Markov chain, the change in occupation number in state A is correlated to the change in occupation number in state B if and only if A and B are directly connected. This implies that if we are only interested in state A, fluctuations in B may be replaced with their mean if state B is not directly connected to A, which shortens computing time considerably. We show the accuracy and efficacy of our approximation theoretically and in simulations of stochastic ion-channel gating in neurons.

Markov Chain Ontology Analysis (MCOA)

PubMed Central

2012-01-01

Background Biomedical ontologies have become an increasingly critical lens through which researchers analyze the genomic, clinical and bibliographic data that fuels scientific research. Of particular relevance are methods, such as enrichment analysis, that quantify the importance of ontology classes relative to a collection of domain data. Current analytical techniques, however, remain limited in their ability to handle many important types of structural complexity encountered in real biological systems including class overlaps, continuously valued data, inter-instance relationships, non-hierarchical relationships between classes, semantic distance and sparse data. Results In this paper, we describe a methodology called Markov Chain Ontology Analysis (MCOA) and illustrate its use through a MCOA-based enrichment analysis application based on a generative model of gene activation. MCOA models the classes in an ontology, the instances from an associated dataset and all directional inter-class, class-to-instance and inter-instance relationships as a single finite ergodic Markov chain. The adjusted transition probability matrix for this Markov chain enables the calculation of eigenvector values that quantify the importance of each ontology class relative to other classes and the associated data set members. On both controlled Gene Ontology (GO) data sets created with Escherichia coli, Drosophila melanogaster and Homo sapiens annotations and real gene expression data extracted from the Gene Expression Omnibus (GEO), the MCOA enrichment analysis approach provides the best performance of comparable state-of-the-art methods. Conclusion A methodology based on Markov chain models and network analytic metrics can help detect the relevant signal within large, highly interdependent and noisy data sets and, for applications such as enrichment analysis, has been shown to generate superior performance on both real and simulated data relative to existing state-of-the-art approaches. PMID:22300537

Markov Chain Ontology Analysis (MCOA).

PubMed

Frost, H Robert; McCray, Alexa T

2012-02-03

Biomedical ontologies have become an increasingly critical lens through which researchers analyze the genomic, clinical and bibliographic data that fuels scientific research. Of particular relevance are methods, such as enrichment analysis, that quantify the importance of ontology classes relative to a collection of domain data. Current analytical techniques, however, remain limited in their ability to handle many important types of structural complexity encountered in real biological systems including class overlaps, continuously valued data, inter-instance relationships, non-hierarchical relationships between classes, semantic distance and sparse data. In this paper, we describe a methodology called Markov Chain Ontology Analysis (MCOA) and illustrate its use through a MCOA-based enrichment analysis application based on a generative model of gene activation. MCOA models the classes in an ontology, the instances from an associated dataset and all directional inter-class, class-to-instance and inter-instance relationships as a single finite ergodic Markov chain. The adjusted transition probability matrix for this Markov chain enables the calculation of eigenvector values that quantify the importance of each ontology class relative to other classes and the associated data set members. On both controlled Gene Ontology (GO) data sets created with Escherichia coli, Drosophila melanogaster and Homo sapiens annotations and real gene expression data extracted from the Gene Expression Omnibus (GEO), the MCOA enrichment analysis approach provides the best performance of comparable state-of-the-art methods. A methodology based on Markov chain models and network analytic metrics can help detect the relevant signal within large, highly interdependent and noisy data sets and, for applications such as enrichment analysis, has been shown to generate superior performance on both real and simulated data relative to existing state-of-the-art approaches.

A Langevin equation for the rates of currency exchange based on the Markov analysis

NASA Astrophysics Data System (ADS)

Farahpour, F.; Eskandari, Z.; Bahraminasab, A.; Jafari, G. R.; Ghasemi, F.; Sahimi, Muhammad; Reza Rahimi Tabar, M.

2007-11-01

We propose a method for analyzing the data for the rates of exchange of various currencies versus the U.S. dollar. The method analyzes the return time series of the data as a Markov process, and develops an effective equation which reconstructs it. We find that the Markov time scale, i.e., the time scale over which the data are Markov-correlated, is one day for the majority of the daily exchange rates that we analyze. We derive an effective Langevin equation to describe the fluctuations in the rates. The equation contains two quantities, D and D, representing the drift and diffusion coefficients, respectively. We demonstrate how the two coefficients are estimated directly from the data, without using any assumptions or models for the underlying stochastic time series that represent the daily rates of exchange of various currencies versus the U.S. dollar.

Decomposition of conditional probability for high-order symbolic Markov chains.

PubMed

Melnik, S S; Usatenko, O V

2017-07-01

The main goal of this paper is to develop an estimate for the conditional probability function of random stationary ergodic symbolic sequences with elements belonging to a finite alphabet. We elaborate on a decomposition procedure for the conditional probability function of sequences considered to be high-order Markov chains. We represent the conditional probability function as the sum of multilinear memory function monomials of different orders (from zero up to the chain order). This allows us to introduce a family of Markov chain models and to construct artificial sequences via a method of successive iterations, taking into account at each step increasingly high correlations among random elements. At weak correlations, the memory functions are uniquely expressed in terms of the high-order symbolic correlation functions. The proposed method fills the gap between two approaches, namely the likelihood estimation and the additive Markov chains. The obtained results may have applications for sequential approximation of artificial neural network training.

A high-fidelity weather time series generator using the Markov Chain process on a piecewise level

NASA Astrophysics Data System (ADS)

Hersvik, K.; Endrerud, O.-E. V.

2017-12-01

A method is developed for generating a set of unique weather time-series based on an existing weather series. The method allows statistically valid weather variations to take place within repeated simulations of offshore operations. The numerous generated time series need to share the same statistical qualities as the original time series. Statistical qualities here refer mainly to the distribution of weather windows available for work, including durations and frequencies of such weather windows, and seasonal characteristics. The method is based on the Markov chain process. The core new development lies in how the Markov Process is used, specifically by joining small pieces of random length time series together rather than joining individual weather states, each from a single time step, which is a common solution found in the literature. This new Markov model shows favorable characteristics with respect to the requirements set forth and all aspects of the validation performed.

Decomposition of conditional probability for high-order symbolic Markov chains

NASA Astrophysics Data System (ADS)

Melnik, S. S.; Usatenko, O. V.

2017-07-01

The main goal of this paper is to develop an estimate for the conditional probability function of random stationary ergodic symbolic sequences with elements belonging to a finite alphabet. We elaborate on a decomposition procedure for the conditional probability function of sequences considered to be high-order Markov chains. We represent the conditional probability function as the sum of multilinear memory function monomials of different orders (from zero up to the chain order). This allows us to introduce a family of Markov chain models and to construct artificial sequences via a method of successive iterations, taking into account at each step increasingly high correlations among random elements. At weak correlations, the memory functions are uniquely expressed in terms of the high-order symbolic correlation functions. The proposed method fills the gap between two approaches, namely the likelihood estimation and the additive Markov chains. The obtained results may have applications for sequential approximation of artificial neural network training.

Canonical Structure and Orthogonality of Forces and Currents in Irreversible Markov Chains

NASA Astrophysics Data System (ADS)

Kaiser, Marcus; Jack, Robert L.; Zimmer, Johannes

2018-03-01

We discuss a canonical structure that provides a unifying description of dynamical large deviations for irreversible finite state Markov chains (continuous time), Onsager theory, and Macroscopic Fluctuation Theory (MFT). For Markov chains, this theory involves a non-linear relation between probability currents and their conjugate forces. Within this framework, we show how the forces can be split into two components, which are orthogonal to each other, in a generalised sense. This splitting allows a decomposition of the pathwise rate function into three terms, which have physical interpretations in terms of dissipation and convergence to equilibrium. Similar decompositions hold for rate functions at level 2 and level 2.5. These results clarify how bounds on entropy production and fluctuation theorems emerge from the underlying dynamical rules. We discuss how these results for Markov chains are related to similar structures within MFT, which describes hydrodynamic limits of such microscopic models.

Screening for latent and active tuberculosis infection in the elderly at admission to residential care homes: A cost-effectiveness analysis in an intermediate disease burden area.

PubMed

Li, Jun; Yip, Benjamin H K; Leung, Chichiu; Chung, Wankyo; Kwok, Kin On; Chan, Emily Y Y; Yeoh, Engkiong; Chung, Puihong

2018-01-01

Tuberculosis (TB) in the elderly remains a challenge in intermediate disease burden areas like Hong Kong. Given a higher TB burden in the elderly and limited impact of current case-finding strategy by patient-initiated pathway, proactive screening approaches for the high-risk group could be optimal and increasingly need targeted economic evaluations. In this study, we examined whether and under what circumstance the screening strategies are cost-effective compared with no screening strategy for the elderly at admission to residential care homes. A decision analytic process based on Markov model was adopted to evaluate the cost-effectiveness of four strategies: (i) no screening, (ii) TB screening (CXR) and (iii) TB screening (Xpert) represent screening for TB in symptomatic elderly by chest X-ray and Xpert® MTB/RIF respectively, and (iv) LTBI/TB screening represents screening for latent and active TB infection by QuantiFERON®-TB Gold In-Tube and chest X-ray. The target population was a hypothetical cohort of 65-year-old people, using a health service provider perspective and a time horizon of 20 years. The outcomes were direct medical costs, life-years and quality-adjusted life-years (QALYs) measured by incremental cost-effectiveness ratio (ICER). In the base-case analysis, no screening was the most cost-saving; TB screening (CXR) was dominated by TB screening (Xpert); LTBI/TB screening resulted in more life-years and QALYs accrued. The ICERs of LTBI/TB screening were US$19,712 and US$29,951 per QALY gained compared with no screening and TB screening (Xpert), respectively. At the willingness-to-pay threshold of US$50,000 per QALY gained, LTBI/TB screening was the most cost-effective when the probability of annual LTBI reactivation was greater than 0.155% and acceptability of LTBI/TB screening was greater than 38%. In 1,000 iterations of Monte Carlo simulation, the probabilities of no screening, TB screening (CXR), TB screening (Xpert), and LTBI/TB screening to be cost-effective were 0, 1.3%, 20.1%, and 78.6% respectively. Screening for latent and active TB infection in Hong Kong elderly people at admission to residential care homes appears to be highly effective and cost-effective. The key findings may be the next key factor to bring down TB endemic in the elderly population among intermediate TB burden areas.

Markov Chains For Testing Redundant Software

NASA Technical Reports Server (NTRS)

White, Allan L.; Sjogren, Jon A.

1990-01-01

Preliminary design developed for validation experiment that addresses problems unique to assuring extremely high quality of multiple-version programs in process-control software. Approach takes into account inertia of controlled system in sense it takes more than one failure of control program to cause controlled system to fail. Verification procedure consists of two steps: experimentation (numerical simulation) and computation, with Markov model for each step.

A Network of Conformational Transitions in the Apo Form of NDM-1 Enzyme Revealed by MD Simulation and a Markov State Model.

PubMed

Gao, Kaifu; Zhao, Yunjie

2017-04-13

New Delhi metallo-β-lactamase-1 (NDM-1) is a novel β-lactamase enzyme that confers enteric bacteria with nearly complete resistance to all β-lactam antibiotics, so it raises a formidable and global threat to human health. However, the binding mechanism between apo-NDM-1 and antibiotics as well as related conformational changes remains poorly understood, which largely hinders the overcoming of its antibiotic resistance. In our study, long-time conventional molecular dynamics simulation and Markov state models were applied to reveal both the dynamical and conformational landscape of apo-NDM-1: the MD simulation demonstrates that loop L3, which is responsible for antibiotic binding, is the most flexible and undergoes dramatic conformational changes; moreover, the Markov state model built from the simulation maps four metastable states including open, semiopen, and closed conformations of loop L3 as well as frequent transitions between the states. Our findings propose a possible conformational selection model for the binding mechanism between apo-NDM-1 and antibiotics, which facilitates the design of novel inhibitors and antibiotics.

Risk assessment by dynamic representation of vulnerability, exploitation, and impact

NASA Astrophysics Data System (ADS)

Cam, Hasan

2015-05-01

Assessing and quantifying cyber risk accurately in real-time is essential to providing security and mission assurance in any system and network. This paper presents a modeling and dynamic analysis approach to assessing cyber risk of a network in real-time by representing dynamically its vulnerabilities, exploitations, and impact using integrated Bayesian network and Markov models. Given the set of vulnerabilities detected by a vulnerability scanner in a network, this paper addresses how its risk can be assessed by estimating in real-time the exploit likelihood and impact of vulnerability exploitation on the network, based on real-time observations and measurements over the network. The dynamic representation of the network in terms of its vulnerabilities, sensor measurements, and observations is constructed dynamically using the integrated Bayesian network and Markov models. The transition rates of outgoing and incoming links of states in hidden Markov models are used in determining exploit likelihood and impact of attacks, whereas emission rates help quantify the attack states of vulnerabilities. Simulation results show the quantification and evolving risk scores over time for individual and aggregated vulnerabilities of a network.

Post processing of optically recognized text via second order hidden Markov model

NASA Astrophysics Data System (ADS)

Poudel, Srijana

In this thesis, we describe a postprocessing system on Optical Character Recognition(OCR) generated text. Second Order Hidden Markov Model (HMM) approach is used to detect and correct the OCR related errors. The reason for choosing the 2nd order HMM is to keep track of the bigrams so that the model can represent the system more accurately. Based on experiments with training data of 159,733 characters and testing of 5,688 characters, the model was able to correct 43.38 % of the errors with a precision of 75.34 %. However, the precision value indicates that the model introduced some new errors, decreasing the correction percentage to 26.4%.

Exact Markov chains versus diffusion theory for haploid random mating.

PubMed

Tyvand, Peder A; Thorvaldsen, Steinar

2010-05-01

Exact discrete Markov chains are applied to the Wright-Fisher model and the Moran model of haploid random mating. Selection and mutations are neglected. At each discrete value of time t there is a given number n of diploid monoecious organisms. The evolution of the population distribution is given in diffusion variables, to compare the two models of random mating with their common diffusion limit. Only the Moran model converges uniformly to the diffusion limit near the boundary. The Wright-Fisher model allows the population size to change with the generations. Diffusion theory tends to under-predict the loss of genetic information when a population enters a bottleneck. 2010 Elsevier Inc. All rights reserved.

[Succession caused by beaver (Castor fiber L.) life activity: I. What is learnt from the calibration of a simple Markov model].

PubMed

Logofet, D O; Evstigneev, O I; Aleĭnikov, A A; Morozova, A O

2014-01-01

A homogeneous Markov chain of three aggregated states "pond--swamp--wood" is proposed as a model of cyclic zoogenic successions caused by beaver (Castor fiber L.) life activity in a forest biogeocoenosis. To calibrate the chain transition matrix, the data have appeared sufficient that were gained from field studies undertaken in "Bryanskii Les" Reserve in the years of 2002-2008. Major outcomes of the calibrated model ensue from the formulae of finite homogeneous Markov chain theory: the stationary probability distribution of states, thematrix (T) of mean first passage times, and the mean durations (M(j)) of succession stages. The former illustrates the distribution of relative areas under succession stages if the current trends and transition rates of succession are conserved in the long-term--it has appeared close to the observed distribution. Matrix T provides for quantitative characteristics of the cyclic process, specifying the ranges the experts proposed for the duration of stages in the conceptual scheme of succession. The calculated values of M(j) detect potential discrepancies between empirical data, the expert knowledge that summarizes the data, and the postulates accepted in the mathematical model. The calculated M2 value falls outside the expert range, which gives a reason to doubt the validity of expert estimation proposed, the aggregation mode chosen for chain states, or/and the accuracy-of data available, i.e., to draw certain "lessons" from partially successful calibration. Refusal to postulate the time homogeneity or the Markov property of the chain is also discussed among possible ways to improve the model.

Utilization of two web-based continuing education courses evaluated by Markov chain model.

PubMed

Tian, Hao; Lin, Jin-Mann S; Reeves, William C

2012-01-01

To evaluate the web structure of two web-based continuing education courses, identify problems and assess the effects of web site modifications. Markov chain models were built from 2008 web usage data to evaluate the courses' web structure and navigation patterns. The web site was then modified to resolve identified design issues and the improvement in user activity over the subsequent 12 months was quantitatively evaluated. Web navigation paths were collected between 2008 and 2010. The probability of navigating from one web page to another was analyzed. The continuing education courses' sequential structure design was clearly reflected in the resulting actual web usage models, and none of the skip transitions provided was heavily used. The web navigation patterns of the two different continuing education courses were similar. Two possible design flaws were identified and fixed in only one of the two courses. Over the following 12 months, the drop-out rate in the modified course significantly decreased from 41% to 35%, but remained unchanged in the unmodified course. The web improvement effects were further verified via a second-order Markov chain model. The results imply that differences in web content have less impact than web structure design on how learners navigate through continuing education courses. Evaluation of user navigation can help identify web design flaws and guide modifications. This study showed that Markov chain models provide a valuable tool to evaluate web-based education courses. Both the results and techniques in this study would be very useful for public health education and research specialists.

Utilization of two web-based continuing education courses evaluated by Markov chain model

PubMed Central

Lin, Jin-Mann S; Reeves, William C

2011-01-01

Objectives To evaluate the web structure of two web-based continuing education courses, identify problems and assess the effects of web site modifications. Design Markov chain models were built from 2008 web usage data to evaluate the courses' web structure and navigation patterns. The web site was then modified to resolve identified design issues and the improvement in user activity over the subsequent 12 months was quantitatively evaluated. Measurements Web navigation paths were collected between 2008 and 2010. The probability of navigating from one web page to another was analyzed. Results The continuing education courses' sequential structure design was clearly reflected in the resulting actual web usage models, and none of the skip transitions provided was heavily used. The web navigation patterns of the two different continuing education courses were similar. Two possible design flaws were identified and fixed in only one of the two courses. Over the following 12 months, the drop-out rate in the modified course significantly decreased from 41% to 35%, but remained unchanged in the unmodified course. The web improvement effects were further verified via a second-order Markov chain model. Conclusions The results imply that differences in web content have less impact than web structure design on how learners navigate through continuing education courses. Evaluation of user navigation can help identify web design flaws and guide modifications. This study showed that Markov chain models provide a valuable tool to evaluate web-based education courses. Both the results and techniques in this study would be very useful for public health education and research specialists. PMID:21976027

Cost-effectiveness analysis of adding a quadrivalent HPV vaccine to the cervical cancer screening programme in Switzerland.

PubMed

Szucs, Thomas D; Largeron, Nathalie; Dedes, Konstantin J; Rafia, Rachid; Bénard, Stève

2008-05-01

Based on positive safety and efficacy data, a quadrivalent Human PapillomaVirus (HPV) vaccine has been approved in Switzerland to prevent HPV types 6, 11, 16 and 18 infections. The objective of this study was to explore the cost-effectiveness of an HPV vaccination in Switzerland. A Markov model of the natural history of HPV infection was adapted to the Swiss context and followed a hypothetical cohort of 41,200 girls aged 11 years over their lifetime. Main epidemiological and economic parameters were extracted from the literature. Two strategies were compared: conventional cytological screening only and HPV vaccination followed by conventional cytological screening. A coverage rate of 80% was used and the vaccine was assumed to provide a lifelong protection. Analyses were performed from the direct health care cost perspective including only direct medical costs. Compared to screening only, adding a quadrivalent HPV vaccine could prevent over lifetime 62% of cervical cancers and related deaths, 19% of Cervical Intraepithelial Neoplasia (CIN 1), 43% of CIN 2, 45% of CIN 3 and 66% of genital warts per cohort. Incremental cost-effectiveness ratios (ICER) were estimated to be CHF 45,008 per Life Year Gained (LYG) and CHF 26,005 per Quality Adjusted Life Year (QALY) gained. Sensitivity analyses demonstrated that the ICER was robust to all parameters, but was most sensitive to the need for a booster and discount rates. Compared to commonly accepted standard thresholds in Europe and other vaccination strategies implemented in Switzerland, adding a quadrivalent HPV vaccine alongside the current cervical cancer screening programme is likely to be cost-effective in Switzerland.

Cost-effectiveness of additional blood screening tests in the Netherlands.

PubMed

Borkent-Raven, Barbara A; Janssen, Mart P; van der Poel, Cees L; Bonsel, Gouke J; van Hout, Ben A

2012-03-01

During the past decade, blood screening tests such as triplex nucleic acid amplification testing (NAT) and human T-cell lymphotropic virus type I or I (HTLV-I/II) antibody testing were added to existing serologic testing for hepatitis B virus (HBV), human immunodeficiency virus (HIV), and hepatitis C virus (HCV). In some low-prevalence regions these additional tests yielded disputable benefits that can be valuated by cost-effectiveness analyses (CEAs). CEAs are used to support decision making on implementation of medical technology. We present CEAs of selected additional screening tests that are not uniformly implemented in the EU. Cost-effectiveness was analyzed of: 1) HBV, HCV, and HIV triplex NAT in addition to serologic testing; 2) HTLV-I/II antibody test for all donors, for first-time donors only, and for pediatric recipients only; and 3) hepatitis A virus (HAV) for all donations. Disease progression of the studied viral infections was described in five Markov models. In the Netherlands, the incremental cost-effectiveness ratio (ICER) of triplex NAT is €5.20 million per quality-adjusted life-year (QALY) for testing minipools of six donation samples and €4.65 million/QALY for individual donation testing. The ICER for anti-HTLV-I/II is €45.2 million/QALY if testing all donations, €2.23 million/QALY if testing new donors only, and €27.0 million/QALY if testing blood products for pediatric patients only. The ICER of HAV NAT is €18.6 million/QALY. The resulting ICERs are very high, especially when compared to other health care interventions. Nevertheless, these screening tests are implemented in the Netherlands and elsewhere. Policy makers should reflect more explicit on the acceptability of costs and effects whenever additional blood screening tests are implemented. © 2011 American Association of Blood Banks.

Modelling past land use using archaeological and pollen data

NASA Astrophysics Data System (ADS)

Pirzamanbein, Behnaz; Lindström, johan; Poska, Anneli; Gaillard-Lemdahl, Marie-José

2016-04-01

Accurate maps of past land use are necessary for studying the impact of anthropogenic land-cover changes on climate and biodiversity. We develop a Bayesian hierarchical model to reconstruct the land use using Gaussian Markov random fields. The model uses two observations sets: 1) archaeological data, representing human settlements, urbanization and agricultural findings; and 2) pollen-based land estimates of the three land-cover types Coniferous forest, Broadleaved forest and Unforested/Open land. The pollen based estimates are obtained from the REVEALS model, based on pollen counts from lakes and bogs. Our developed model uses the sparse pollen-based estimations to reconstruct the spatial continuous cover of three land cover types. Using the open-land component and the archaeological data, the extent of land-use is reconstructed. The model is applied on three time periods - centred around 1900 CE, 1000 and, 4000 BCE over Sweden for which both pollen-based estimates and archaeological data are available. To estimate the model parameters and land use, a block updated Markov chain Monte Carlo (MCMC) algorithm is applied. Using the MCMC posterior samples uncertainties in land-use predictions are computed. Due to lack of good historic land use data, model results are evaluated by cross-validation. Keywords. Spatial reconstruction, Gaussian Markov random field, Fossil pollen records, Archaeological data, Human land-use, Prediction uncertainty

Use of risk projection models to estimate mortality and incidence from radiation-induced breast cancer in screening programs

NASA Astrophysics Data System (ADS)

Ramos, M.; Ferrer, S.; Villaescusa, J. I.; Verdú, G.; Salas, M. D.; Cuevas, M. D.

2005-02-01

The authors report on a method to calculate radiological risks, applicable to breast screening programs and other controlled medical exposures to ionizing radiation. In particular, it has been applied to make a risk assessment in the Valencian Breast Cancer Early Detection Program (VBCEDP) in Spain. This method is based on a parametric approach, through Markov processes, of hazard functions for radio-induced breast cancer incidence and mortality, with mean glandular breast dose, attained age and age-at-exposure as covariates. Excess relative risk functions of breast cancer mortality have been obtained from two different case-control studies exposed to ionizing radiation, with different follow-up time: the Canadian Fluoroscopy Cohort Study (1950-1987) and the Life Span Study (1950-1985 and 1950-1990), whereas relative risk functions for incidence have been obtained from the Life Span Study (1958-1993), the Massachusetts tuberculosis cohorts (1926-1985 and 1970-1985), the New York post-partum mastitis patients (1930-1981) and the Swedish benign breast disease cohort (1958-1987). Relative risks from these cohorts have been transported to the target population undergoing screening in the Valencian Community, a region in Spain with about four and a half million inhabitants. The SCREENRISK software has been developed to estimate radiological detriments in breast screening. Some hypotheses corresponding to different screening conditions have been considered in order to estimate the total risk associated with a woman who takes part in all screening rounds. In the case of the VBCEDP, the total radio-induced risk probability for fatal breast cancer is in a range between [5 × 10-6, 6 × 10-4] versus the natural rate of dying from breast cancer in the Valencian Community which is 9.2 × 10-3. The results show that these indicators could be included in quality control tests and could be adequate for making comparisons between several screening programs.

A male-specific QTL for social interaction behavior in mice mapped with automated pattern detection by a hidden Markov model incorporated into newly developed freeware.

PubMed

Arakawa, Toshiya; Tanave, Akira; Ikeuchi, Shiho; Takahashi, Aki; Kakihara, Satoshi; Kimura, Shingo; Sugimoto, Hiroki; Asada, Nobuhiko; Shiroishi, Toshihiko; Tomihara, Kazuya; Tsuchiya, Takashi; Koide, Tsuyoshi

2014-08-30

Owing to their complex nature, social interaction tests normally require the observation of video data by a human researcher, and thus are difficult to use in large-scale studies. We previously established a statistical method, a hidden Markov model (HMM), which enables the differentiation of two social states ("interaction" and "indifference"), and three social states ("sniffing", "following", and "indifference"), automatically in silico. Here, we developed freeware called DuoMouse for the rapid evaluation of social interaction behavior. This software incorporates five steps: (1) settings, (2) video recording, (3) tracking from the video data, (4) HMM analysis, and (5) visualization of the results. Using DuoMouse, we mapped a genetic locus related to social interaction. We previously reported that a consomic strain, B6-Chr6C(MSM), with its chromosome 6 substituted for one from MSM/Ms, showed more social interaction than C57BL/6 (B6). We made four subconsomic strains, C3, C5, C6, and C7, each of which has a shorter segment of chromosome 6 derived from B6-Chr6C, and conducted social interaction tests on these strains. DuoMouse indicated that C6, but not C3, C5, and C7, showed higher interaction, sniffing, and following than B6, specifically in males. The data obtained by human observation showed high concordance to those from DuoMouse. The results indicated that the MSM-derived chromosomal region present in C6-but not in C3, C5, and C7-associated with increased social behavior. This method to analyze social interaction will aid primary screening for difference in social behavior in mice. Copyright © 2014 Elsevier B.V. All rights reserved.

Alignment-free Transcriptomic and Metatranscriptomic Comparison Using Sequencing Signatures with Variable Length Markov Chains.

PubMed

Liao, Weinan; Ren, Jie; Wang, Kun; Wang, Shun; Zeng, Feng; Wang, Ying; Sun, Fengzhu

2016-11-23

The comparison between microbial sequencing data is critical to understand the dynamics of microbial communities. The alignment-based tools analyzing metagenomic datasets require reference sequences and read alignments. The available alignment-free dissimilarity approaches model the background sequences with Fixed Order Markov Chain (FOMC) yielding promising results for the comparison of microbial communities. However, in FOMC, the number of parameters grows exponentially with the increase of the order of Markov Chain (MC). Under a fixed high order of MC, the parameters might not be accurately estimated owing to the limitation of sequencing depth. In our study, we investigate an alternative to FOMC to model background sequences with the data-driven Variable Length Markov Chain (VLMC) in metatranscriptomic data. The VLMC originally designed for long sequences was extended to apply to high-throughput sequencing reads and the strategies to estimate the corresponding parameters were developed. The flexible number of parameters in VLMC avoids estimating the vast number of parameters of high-order MC under limited sequencing depth. Different from the manual selection in FOMC, VLMC determines the MC order adaptively. Several beta diversity measures based on VLMC were applied to compare the bacterial RNA-Seq and metatranscriptomic datasets. Experiments show that VLMC outperforms FOMC to model the background sequences in transcriptomic and metatranscriptomic samples. A software pipeline is available at https://d2vlmc.codeplex.com.

Stability Analysis of Multi-Sensor Kalman Filtering over Lossy Networks

PubMed Central

Gao, Shouwan; Chen, Pengpeng; Huang, Dan; Niu, Qiang

2016-01-01

This paper studies the remote Kalman filtering problem for a distributed system setting with multiple sensors that are located at different physical locations. Each sensor encapsulates its own measurement data into one single packet and transmits the packet to the remote filter via a lossy distinct channel. For each communication channel, a time-homogeneous Markov chain is used to model the normal operating condition of packet delivery and losses. Based on the Markov model, a necessary and sufficient condition is obtained, which can guarantee the stability of the mean estimation error covariance. Especially, the stability condition is explicitly expressed as a simple inequality whose parameters are the spectral radius of the system state matrix and transition probabilities of the Markov chains. In contrast to the existing related results, our method imposes less restrictive conditions on systems. Finally, the results are illustrated by simulation examples. PMID:27104541

Pattern statistics on Markov chains and sensitivity to parameter estimation

PubMed Central

Nuel, Grégory

2006-01-01

Background: In order to compute pattern statistics in computational biology a Markov model is commonly used to take into account the sequence composition. Usually its parameter must be estimated. The aim of this paper is to determine how sensitive these statistics are to parameter estimation, and what are the consequences of this variability on pattern studies (finding the most over-represented words in a genome, the most significant common words to a set of sequences,...). Results: In the particular case where pattern statistics (overlap counting only) computed through binomial approximations we use the delta-method to give an explicit expression of σ, the standard deviation of a pattern statistic. This result is validated using simulations and a simple pattern study is also considered. Conclusion: We establish that the use of high order Markov model could easily lead to major mistakes due to the high sensitivity of pattern statistics to parameter estimation. PMID:17044916

Variable context Markov chains for HIV protease cleavage site prediction.

PubMed

Oğul, Hasan

2009-06-01

Deciphering the knowledge of HIV protease specificity and developing computational tools for detecting its cleavage sites in protein polypeptide chain are very desirable for designing efficient and specific chemical inhibitors to prevent acquired immunodeficiency syndrome. In this study, we developed a generative model based on a generalization of variable order Markov chains (VOMC) for peptide sequences and adapted the model for prediction of their cleavability by certain proteases. The new method, called variable context Markov chains (VCMC), attempts to identify the context equivalence based on the evolutionary similarities between individual amino acids. It was applied for HIV-1 protease cleavage site prediction problem and shown to outperform existing methods in terms of prediction accuracy on a common dataset. In general, the method is a promising tool for prediction of cleavage sites of all proteases and encouraged to be used for any kind of peptide classification problem as well.

Pattern statistics on Markov chains and sensitivity to parameter estimation.

PubMed

Nuel, Grégory

2006-10-17

In order to compute pattern statistics in computational biology a Markov model is commonly used to take into account the sequence composition. Usually its parameter must be estimated. The aim of this paper is to determine how sensitive these statistics are to parameter estimation, and what are the consequences of this variability on pattern studies (finding the most over-represented words in a genome, the most significant common words to a set of sequences,...). In the particular case where pattern statistics (overlap counting only) computed through binomial approximations we use the delta-method to give an explicit expression of sigma, the standard deviation of a pattern statistic. This result is validated using simulations and a simple pattern study is also considered. We establish that the use of high order Markov model could easily lead to major mistakes due to the high sensitivity of pattern statistics to parameter estimation.

Cost effectiveness and projected national impact of colorectal cancer screening in France.

PubMed

Hassan, C; Benamouzig, R; Spada, C; Ponchon, T; Zullo, A; Saurin, J C; Costamagna, G

2011-09-01

Colorectal cancer (CRC) is a major cause of morbidity and mortality in France. Only scanty data on cost-effectiveness of CRC screening in Europe are available, generating uncertainty over its efficiency. Although immunochemical fecal tests (FIT) and guaiac-based fecal occult blood tests (g-FOBT) have been shown to be cost-effective in France, cost-effectiveness of endoscopic screening has not yet been addressed. Cost-effectiveness of screening strategies using colonoscopy, flexible sigmoidoscopy, second-generation colon capsule endoscopy (CCE), FIT and g-FOBT were compared using a Markov model. A 40 % adherence rate was assumed for all strategies. Colonoscopy costs included anesthesiologist assistance. Incremental cost-effectiveness ratios (ICERs) were calculated. Probabilistic and value-of-information analyses were used to estimate the expected benefit of future research. A third-payer perspective was adopted. In the reference case analysis, FIT repeated every year was the most cost-effective strategy, with an ICER of €48165 per life-year gained vs. FIT every 2 years, which was the next most cost-effective strategy. Although CCE every 5 years was as effective as FIT 1-year, it was not a cost-effective alternative. Colonoscopy repeated every 10 years was substantially more costly, and slightly less effective than FIT 1-year. When projecting the model outputs onto the French population, the least (g-FOBT 2-years) and most (FIT 1-year) effective strategies reduced the absolute number of annual CRC deaths from 16037 to 12916 and 11217, respectively, resulting in an annual additional cost of €26 million and €347 million, respectively. Probabilistic sensitivity analysis demonstrated that FIT 1-year was the optimal choice in 20% of the simulated scenarios, whereas sigmoidoscopy 5-years, colonoscopy, and FIT 2-years were the optimal choices in 40%, 26%, and 14%, respectively. A screening program based on FIT 1-year appeared to be the most cost-effective approach for CRC screening in France. However, a substantial uncertainty over this choice is still present. © Georg Thieme Verlag KG Stuttgart · New York.

A cost-utility analysis of cervical cancer screening and human papillomavirus vaccination in the Philippines.

PubMed

Guerrero, Anna Melissa; Genuino, Anne Julienne; Santillan, Melanie; Praditsitthikorn, Naiyana; Chantarastapornchit, Varit; Teerawattananon, Yot; Alejandria, Marissa; Toral, Jean Anne

2015-07-30

Cervical cancer is the second leading cause of cancer cases and deaths among Filipino women because of inadequate access to screening and treatment services. This study aims to evaluate the health and economic benefits of HPV vaccination and its combination with different screening strategies to find the most optimal preventive strategy in the Philippines. A cost-utility analysis was conducted using an existing semi-Markov model to evaluate different screening (i.e., Pap smear, visual inspection with acetic acid) and vaccination strategies against HPV infection implemented alone or as part of a combination strategy at different coverage scenarios. The model was run using country-specific epidemiologic, cost and clinical parameters from a health system perspective. Sensitivity analysis was performed for vaccine efficacy, duration of protection and costs of vaccination, screening and treatment. Across all coverage scenarios, VIA has been shown to be a dominant and cost-saving screening strategy with incremental cost-effectiveness ratio (ICER) ranging from dominant to Php 61,059 (1443 USD) per QALY gained. VIA can reduce cervical cancer cases and deaths by 25%. Pap smear screening was found to be not cost-effective due to its high cost in the Philippines. Adding HPV vaccination at a cost of 54 USD per vaccinated girl on top of VIA screening was found to be potentially cost-effective using a threshold of 1 GDP per capita (i.e., Php 120,000 or 2835 USD/ QALY) with the most favorable assumption of providing lifelong immunity against high-risk oncogenic HPV types 16/18. The highest incremental QALY gain was achieved with 80% coverage of the combined strategy of VIA at 35 to 45 years old done every five years following vaccination at 11 years of age with an ICER of Php 33,126 (783 USD). This strategy may result in a two-thirds reduction in cervical cancer burden. HPV vaccination is not cost-effective when vaccine protection lasts for less than 20 years. High VIA coverage targeting women aged 35-45 years old at five-year intervals is the most efficient and cost-saving strategy in reducing cervical cancer burden in the Philippines. Adding a vaccination program at high coverage among 11-year-old girls is potentially cost-effective in the Philippines assuming a life-long duration of vaccine efficacy.

Multivariate Markov chain modeling for stock markets

NASA Astrophysics Data System (ADS)

Maskawa, Jun-ichi

2003-06-01

We study a multivariate Markov chain model as a stochastic model of the price changes of portfolios in the framework of the mean field approximation. The time series of price changes are coded into the sequences of up and down spins according to their signs. We start with the discussion for small portfolios consisting of two stock issues. The generalization of our model to arbitrary size of portfolio is constructed by a recurrence relation. The resultant form of the joint probability of the stationary state coincides with Gibbs measure assigned to each configuration of spin glass model. Through the analysis of actual portfolios, it has been shown that the synchronization of the direction of the price changes is well described by the model.

A Unified Framework for Complex Networks with Degree Trichotomy Based on Markov Chains.

PubMed

Hui, David Shui Wing; Chen, Yi-Chao; Zhang, Gong; Wu, Weijie; Chen, Guanrong; Lui, John C S; Li, Yingtao

2017-06-16

This paper establishes a Markov chain model as a unified framework for describing the evolution processes in complex networks. The unique feature of the proposed model is its capability in addressing the formation mechanism that can reflect the "trichotomy" observed in degree distributions, based on which closed-form solutions can be derived. Important special cases of the proposed unified framework are those classical models, including Poisson, Exponential, Power-law distributed networks. Both simulation and experimental results demonstrate a good match of the proposed model with real datasets, showing its superiority over the classical models. Implications of the model to various applications including citation analysis, online social networks, and vehicular networks design, are also discussed in the paper.

Reciprocal Markov Modeling of Feedback Mechanisms Between Emotion and Dietary Choice Using Experience-Sampling Data.

PubMed

Lu, Ji; Pan, Junhao; Zhang, Qiang; Dubé, Laurette; Ip, Edward H

2015-01-01

With intensively collected longitudinal data, recent advances in the experience-sampling method (ESM) benefit social science empirical research, but also pose important methodological challenges. As traditional statistical models are not generally well equipped to analyze a system of variables that contain feedback loops, this paper proposes the utility of an extended hidden Markov model to model reciprocal the relationship between momentary emotion and eating behavior. This paper revisited an ESM data set (Lu, Huet, & Dube, 2011) that observed 160 participants' food consumption and momentary emotions 6 times per day in 10 days. Focusing on the analyses on feedback loop between mood and meal-healthiness decision, the proposed reciprocal Markov model (RMM) can accommodate both hidden ("general" emotional states: positive vs. negative state) and observed states (meal: healthier, same or less healthy than usual) without presuming independence between observations and smooth trajectories of mood or behavior changes. The results of RMM analyses illustrated the reciprocal chains of meal consumption and mood as well as the effect of contextual factors that moderate the interrelationship between eating and emotion. A simulation experiment that generated data consistent with the empirical study further demonstrated that the procedure is promising in terms of recovering the parameters.

Modeling Driver Behavior near Intersections in Hidden Markov Model

PubMed Central

Li, Juan; He, Qinglian; Zhou, Hang; Guan, Yunlin; Dai, Wei

2016-01-01

Intersections are one of the major locations where safety is a big concern to drivers. Inappropriate driver behaviors in response to frequent changes when approaching intersections often lead to intersection-related crashes or collisions. Thus to better understand driver behaviors at intersections, especially in the dilemma zone, a Hidden Markov Model (HMM) is utilized in this study. With the discrete data processing, the observed dynamic data of vehicles are used for the inference of the Hidden Markov Model. The Baum-Welch (B-W) estimation algorithm is applied to calculate the vehicle state transition probability matrix and the observation probability matrix. When combined with the Forward algorithm, the most likely state of the driver can be obtained. Thus the model can be used to measure the stability and risk of driver behavior. It is found that drivers’ behaviors in the dilemma zone are of lower stability and higher risk compared with those in other regions around intersections. In addition to the B-W estimation algorithm, the Viterbi Algorithm is utilized to predict the potential dangers of vehicles. The results can be applied to driving assistance systems to warn drivers to avoid possible accidents. PMID:28009838

Model-based Clustering of Categorical Time Series with Multinomial Logit Classification

NASA Astrophysics Data System (ADS)

Frühwirth-Schnatter, Sylvia; Pamminger, Christoph; Winter-Ebmer, Rudolf; Weber, Andrea

2010-09-01

A common problem in many areas of applied statistics is to identify groups of similar time series in a panel of time series. However, distance-based clustering methods cannot easily be extended to time series data, where an appropriate distance-measure is rather difficult to define, particularly for discrete-valued time series. Markov chain clustering, proposed by Pamminger and Frühwirth-Schnatter [6], is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This model-based clustering method is based on finite mixtures of first-order time-homogeneous Markov chain models. In order to further explain group membership we present an extension to the approach of Pamminger and Frühwirth-Schnatter [6] by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule by using a multinomial logit model. The parameters are estimated for a fixed number of clusters within a Bayesian framework using an Markov chain Monte Carlo (MCMC) sampling scheme representing a (full) Gibbs-type sampler which involves only draws from standard distributions. Finally, an application to a panel of Austrian wage mobility data is presented which leads to an interesting segmentation of the Austrian labour market.

Analyzing Dyadic Sequence Data—Research Questions and Implied Statistical Models

PubMed Central

Fuchs, Peter; Nussbeck, Fridtjof W.; Meuwly, Nathalie; Bodenmann, Guy

2017-01-01

The analysis of observational data is often seen as a key approach to understanding dynamics in romantic relationships but also in dyadic systems in general. Statistical models for the analysis of dyadic observational data are not commonly known or applied. In this contribution, selected approaches to dyadic sequence data will be presented with a focus on models that can be applied when sample sizes are of medium size (N = 100 couples or less). Each of the statistical models is motivated by an underlying potential research question, the most important model results are presented and linked to the research question. The following research questions and models are compared with respect to their applicability using a hands on approach: (I) Is there an association between a particular behavior by one and the reaction by the other partner? (Pearson Correlation); (II) Does the behavior of one member trigger an immediate reaction by the other? (aggregated logit models; multi-level approach; basic Markov model); (III) Is there an underlying dyadic process, which might account for the observed behavior? (hidden Markov model); and (IV) Are there latent groups of dyads, which might account for observing different reaction patterns? (mixture Markov; optimal matching). Finally, recommendations for researchers to choose among the different models, issues of data handling, and advises to apply the statistical models in empirical research properly are given (e.g., in a new r-package “DySeq”). PMID:28443037

Economic evaluation of nivolumab for the treatment of second-line advanced squamous NSCLC in Canada: a comparison of modeling approaches to estimate and extrapolate survival outcomes.

PubMed

Goeree, Ron; Villeneuve, Julie; Goeree, Jeff; Penrod, John R; Orsini, Lucinda; Tahami Monfared, Amir Abbas

2016-06-01

Background Lung cancer is the most common type of cancer in the world and is associated with significant mortality. Nivolumab demonstrated statistically significant improvements in progression-free survival (PFS) and overall survival (OS) for patients with advanced squamous non-small cell lung cancer (NSCLC) who were previously treated. The cost-effectiveness of nivolumab has not been assessed in Canada. A contentious component of projecting long-term cost and outcomes in cancer relates to the modeling approach adopted, with the two most common approaches being partitioned survival (PS) and Markov models. The objectives of this analysis were to estimate the cost-utility of nivolumab and to compare the results using these alternative modeling approaches. Methods Both PS and Markov models were developed using docetaxel and erlotinib as comparators. A three-health state model was used consisting of progression-free, progressed disease, and death. Disease progression and time to progression were estimated by identifying best-fitting survival curves from the clinical trial data for PFS and OS. Expected costs and health outcomes were calculated by combining health-state occupancy with medical resource use and quality-of-life assigned to each of the three health states. The health outcomes included in the model were survival and quality-adjusted-life-years (QALYs). Results Nivolumab was found to have the highest expected per-patient cost, but also improved per-patient life years (LYs) and QALYs. Nivolumab cost an additional $151,560 and $140,601 per QALY gained compared to docetaxel and erlotinib, respectively, using a PS model approach. The cost-utility estimates using a Markov model were very similar ($152,229 and $141,838, respectively, per QALY gained). Conclusions Nivolumab was found to involve a trade-off between improved patient survival and QALYs, and increased cost. It was found that the use of a PS or Markov model produced very similar estimates of expected cost, outcomes, and incremental cost-utility.

Hybrid Discrete-Continuous Markov Decision Processes

NASA Technical Reports Server (NTRS)

Feng, Zhengzhu; Dearden, Richard; Meuleau, Nicholas; Washington, Rich

2003-01-01

This paper proposes a Markov decision process (MDP) model that features both discrete and continuous state variables. We extend previous work by Boyan and Littman on the mono-dimensional time-dependent MDP to multiple dimensions. We present the principle of lazy discretization, and piecewise constant and linear approximations of the model. Having to deal with several continuous dimensions raises several new problems that require new solutions. In the (piecewise) linear case, we use techniques from partially- observable MDPs (POMDPS) to represent value functions as sets of linear functions attached to different partitions of the state space.

Markov Jump-Linear Performance Models for Recoverable Flight Control Computers

NASA Technical Reports Server (NTRS)

Zhang, Hong; Gray, W. Steven; Gonzalez, Oscar R.

2004-01-01

Single event upsets in digital flight control hardware induced by atmospheric neutrons can reduce system performance and possibly introduce a safety hazard. One method currently under investigation to help mitigate the effects of these upsets is NASA Langley s Recoverable Computer System. In this paper, a Markov jump-linear model is developed for a recoverable flight control system, which will be validated using data from future experiments with simulated and real neutron environments. The method of tracking error analysis and the plan for the experiments are also described.

Applications of geostatistics and Markov models for logo recognition

NASA Astrophysics Data System (ADS)

Pham, Tuan

2003-01-01

Spatial covariances based on geostatistics are extracted as representative features of logo or trademark images. These spatial covariances are different from other statistical features for image analysis in that the structural information of an image is independent of the pixel locations and represented in terms of spatial series. We then design a classifier in the sense of hidden Markov models to make use of these geostatistical sequential data to recognize the logos. High recognition rates are obtained from testing the method against a public-domain logo database.

Multi-state Markov model for disability: A case of Malaysia Social Security (SOCSO)

NASA Astrophysics Data System (ADS)

Samsuddin, Shamshimah; Ismail, Noriszura

2016-06-01

Studies of SOCSO's contributor outcomes like disability are usually restricted to a single outcome. In this respect, the study has focused on the approach of multi-state Markov model for estimating the transition probabilities among SOCSO's contributor in Malaysia between states: work, temporary disability, permanent disability and death at yearly intervals on age, gender, year and disability category; ignoring duration and past disability experience which is not consider of how or when someone arrived in that category. These outcomes represent different states which depend on health status among the workers.

Upper and lower bounds for semi-Markov reliability models of reconfigurable systems

NASA Technical Reports Server (NTRS)

White, A. L.

1984-01-01

This paper determines the information required about system recovery to compute the reliability of a class of reconfigurable systems. Upper and lower bounds are derived for these systems. The class consists of those systems that satisfy five assumptions: the components fail independently at a low constant rate, fault occurrence and system reconfiguration are independent processes, the reliability model is semi-Markov, the recovery functions which describe system configuration have small means and variances, and the system is well designed. The bounds are easy to compute, and examples are included.

A Modularized Efficient Framework for Non-Markov Time Series Estimation

NASA Astrophysics Data System (ADS)

Schamberg, Gabriel; Ba, Demba; Coleman, Todd P.

2018-06-01

We present a compartmentalized approach to finding the maximum a-posteriori (MAP) estimate of a latent time series that obeys a dynamic stochastic model and is observed through noisy measurements. We specifically consider modern signal processing problems with non-Markov signal dynamics (e.g. group sparsity) and/or non-Gaussian measurement models (e.g. point process observation models used in neuroscience). Through the use of auxiliary variables in the MAP estimation problem, we show that a consensus formulation of the alternating direction method of multipliers (ADMM) enables iteratively computing separate estimates based on the likelihood and prior and subsequently "averaging" them in an appropriate sense using a Kalman smoother. As such, this can be applied to a broad class of problem settings and only requires modular adjustments when interchanging various aspects of the statistical model. Under broad log-concavity assumptions, we show that the separate estimation problems are convex optimization problems and that the iterative algorithm converges to the MAP estimate. As such, this framework can capture non-Markov latent time series models and non-Gaussian measurement models. We provide example applications involving (i) group-sparsity priors, within the context of electrophysiologic specrotemporal estimation, and (ii) non-Gaussian measurement models, within the context of dynamic analyses of learning with neural spiking and behavioral observations.

Risk aversion and risk seeking in multicriteria forest management: a Markov decision process approach

Treesearch

Joseph Buongiorno; Mo Zhou; Craig Johnston

2017-01-01

Markov decision process models were extended to reflect some consequences of the risk attitude of forestry decision makers. One approach consisted of maximizing the expected value of a criterion subject to an upper bound on the variance or, symmetrically, minimizing the variance subject to a lower bound on the expected value.  The other method used the certainty...

MEGGASENSE - The Metagenome/Genome Annotated Sequence Natural Language Search Engine: A Platform for 
the Construction of Sequence Data Warehouses.

PubMed

Gacesa, Ranko; Zucko, Jurica; Petursdottir, Solveig K; Gudmundsdottir, Elisabet Eik; Fridjonsson, Olafur H; Diminic, Janko; Long, Paul F; Cullum, John; Hranueli, Daslav; Hreggvidsson, Gudmundur O; Starcevic, Antonio

2017-06-01

The MEGGASENSE platform constructs relational databases of DNA or protein sequences. The default functional analysis uses 14 106 hidden Markov model (HMM) profiles based on sequences in the KEGG database. The Solr search engine allows sophisticated queries and a BLAST search function is also incorporated. These standard capabilities were used to generate the SCATT database from the predicted proteome of Streptomyces cattleya . The implementation of a specialised metagenome database (AMYLOMICS) for bioprospecting of carbohydrate-modifying enzymes is described. In addition to standard assembly of reads, a novel 'functional' assembly was developed, in which screening of reads with the HMM profiles occurs before the assembly. The AMYLOMICS database incorporates additional HMM profiles for carbohydrate-modifying enzymes and it is illustrated how the combination of HMM and BLAST analyses helps identify interesting genes. A variety of different proteome and metagenome databases have been generated by MEGGASENSE.

Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoon, Hong-Jun; Alamudun, Folami T.; Hudson, Kathy

Several studies have confirmed that the gaze velocity of the human eye can be utilized as a behavioral biometric or personalized biomarker. In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing breast cancer screening. Using gaze data collected from 10 radiologists reading 100 mammograms of various diagnoses, we compared the performance of a CNN-based classification algorithm with two deep learning classifiers, deep neural network and deep belief network, and a previously presented hidden Markov model classifier. The study showed thatmore » the CNN classifier is superior compared to alternative classification methods based on macro F1-scores derived from 10-fold cross-validation experiments. Our results further support the efficacy of eye gaze velocity as a biometric identifier of medical imaging experts.« less

Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists

DOE PAGES

Yoon, Hong-Jun; Alamudun, Folami T.; Hudson, Kathy; ...

2018-01-24

Several studies have confirmed that the gaze velocity of the human eye can be utilized as a behavioral biometric or personalized biomarker. In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing breast cancer screening. Using gaze data collected from 10 radiologists reading 100 mammograms of various diagnoses, we compared the performance of a CNN-based classification algorithm with two deep learning classifiers, deep neural network and deep belief network, and a previously presented hidden Markov model classifier. The study showed thatmore » the CNN classifier is superior compared to alternative classification methods based on macro F1-scores derived from 10-fold cross-validation experiments. Our results further support the efficacy of eye gaze velocity as a biometric identifier of medical imaging experts.« less

Dimensional Reduction for the General Markov Model on Phylogenetic Trees.

PubMed

Sumner, Jeremy G

2017-03-01

We present a method of dimensional reduction for the general Markov model of sequence evolution on a phylogenetic tree. We show that taking certain linear combinations of the associated random variables (site pattern counts) reduces the dimensionality of the model from exponential in the number of extant taxa, to quadratic in the number of taxa, while retaining the ability to statistically identify phylogenetic divergence events. A key feature is the identification of an invariant subspace which depends only bilinearly on the model parameters, in contrast to the usual multi-linear dependence in the full space. We discuss potential applications including the computation of split (edge) weights on phylogenetic trees from observed sequence data.

Forecasting client transitions in British Columbia's Long-Term Care Program.

PubMed Central

Lane, D; Uyeno, D; Stark, A; Gutman, G; McCashin, B

1987-01-01

This article presents a model for the annual transitions of clients through various home and facility placements in a long-term care program. The model, an application of Markov chain analysis, is developed, tested, and applied to over 9,000 clients (N = 9,483) in British Columbia's Long Term Care Program (LTC) over the period 1978-1983. Results show that the model gives accurate forecasts of the progress of groups of clients from state to state in the long-term care system from time of admission until eventual death. Statistical methods are used to test the modeling hypothesis that clients' year-over-year transitions occur in constant proportions from state to state within the long-term care system. Tests are carried out by examining actual year-over-year transitions of each year's new admission cohort (1978-1983). Various subsets of the available data are analyzed and, after accounting for clear differences among annual cohorts, the most acceptable model of the actual client transition data occurred when clients were separated into male and female groups, i.e., the transition behavior of each group is describable by a different Markov model. To validate the model, we develop model estimates for the numbers of existing clients in each state of the long-term care system for the period (1981-1983) for which actual data are available. When these estimates are compared with the actual data, total weighted absolute deviations do not exceed 10 percent of actuals. Finally, we use the properties of the Markov chain probability transition matrix and simulation methods to develop three-year forecasts with prediction intervals for the distribution of the existing total clients into each state of the system. The tests, forecasts, and Markov model supplemental information are contained in a mechanized procedure suitable for a microcomputer. The procedure provides a powerful, efficient tool for decision makers planning facilities and services in response to the needs of long-term care clients. PMID:3121537

Under-reported data analysis with INAR-hidden Markov chains.

PubMed

Fernández-Fontelo, Amanda; Cabaña, Alejandra; Puig, Pedro; Moriña, David

2016-11-20

In this work, we deal with correlated under-reported data through INAR(1)-hidden Markov chain models. These models are very flexible and can be identified through its autocorrelation function, which has a very simple form. A naïve method of parameter estimation is proposed, jointly with the maximum likelihood method based on a revised version of the forward algorithm. The most-probable unobserved time series is reconstructed by means of the Viterbi algorithm. Several examples of application in the field of public health are discussed illustrating the utility of the models. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Vulnerability of networks of interacting Markov chains.

PubMed

Kocarev, L; Zlatanov, N; Trajanov, D

2010-05-13

The concept of vulnerability is introduced for a model of random, dynamical interactions on networks. In this model, known as the influence model, the nodes are arranged in an arbitrary network, while the evolution of the status at a node is according to an internal Markov chain, but with transition probabilities that depend not only on the current status of that node but also on the statuses of the neighbouring nodes. Vulnerability is treated analytically and numerically for several networks with different topological structures, as well as for two real networks--the network of infrastructures and the EU power grid--identifying the most vulnerable nodes of these networks.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cottam, Joseph A.; Blaha, Leslie M.

Systems have biases. Their interfaces naturally guide a user toward specific patterns of action. For example, modern word-processors and spreadsheets are both capable of taking word wrapping, checking spelling, storing tables, and calculating formulas. You could write a paper in a spreadsheet or could do simple business modeling in a word-processor. However, their interfaces naturally communicate which function they are designed for. Visual analytic interfaces also have biases. In this paper, we outline why simple Markov models are a plausible tool for investigating that bias and how they might be applied. We also discuss some anticipated difficulties in such modelingmore » and touch briefly on what some Markov model extensions might provide.« less

Markov model of the loan portfolio dynamics considering influence of management and external economic factors

NASA Astrophysics Data System (ADS)

Bozhalkina, Yana; Timofeeva, Galina

2016-12-01

Mathematical model of loan portfolio in the form of a controlled Markov chain with discrete time is considered. It is assumed that coefficients of migration matrix depend on corrective actions and external factors. Corrective actions include process of receiving applications, interaction with existing solvent and insolvent clients. External factors are macroeconomic indicators, such as inflation and unemployment rates, exchange rates, consumer price indices, etc. Changes in corrective actions adjust the intensity of transitions in the migration matrix. The mathematical model for forecasting the credit portfolio structure taking into account a cumulative impact of internal and external changes is obtained.

A systematic review of economic models used to assess the cost-effectiveness of strategies for identifying latent tuberculosis in high-risk groups.

PubMed

Auguste, Peter; Tsertsvadze, Alexander; Court, Rachel; Pink, Joshua

2016-07-01

Timely diagnosis and treatment of latent tuberculosis infection (LTBI) through screening remains a key public health priority. Although globally it is recommended to screen people at high risk of developing TB, the economic evidence underpinning these recommendations is limited. This review critically appraised studies that had used a decision-analytical modelling framework to estimate the cost-effectiveness of interferon gamma release assays (IGRAs) compared to tuberculin skin test (TST) for detecting LTBI in high risk populations. A comprehensive search of MEDLINE, EMBASE, NHS-EED was undertaken from 2009 up to June 2015. Studies were screened and extracted by independent reviewers. The study quality was assessed using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) and the Philips' checklist, respectively. A narrative synthesis of the included studies was undertaken. Ten studies were included in this review. Two economic evaluations were conducted in a child population, six in an immunocompromised population and two in a recently arrived population from a country with a high incidence of TB. Most studies (n = 7) used a decision tree structure with Markov nodes. In general, all models were clearly described in terms of reporting quality, but were subject to limitations to structure and model inputs. Models have not elaborated on their setting or the perspective of the studies was not consistent with their analyses. Other concerns were related to derivation of prevalence, test accuracy and transition probabilities. Current methods available highlight limitations in the clinical effectiveness literature, model structures and assumptions, which impact on the robustness of the cost-effectiveness results. These models available are useful, but limited on the information that can be used to inform on future cost-effectiveness analysis. Until consideration is given on deriving the performance of tests used to identify LTBI that progresses to active TB, and the development of more comprehensive models, the economic benefit of LTBI testing with TST/IGRAs in high risk populations will remain unanswered. Copyright © 2016 Elsevier Ltd. All rights reserved.

Stochastic Modeling based on Dictionary Approach for the Generation of Daily Precipitation Occurrences

NASA Astrophysics Data System (ADS)

Panu, U. S.; Ng, W.; Rasmussen, P. F.

2009-12-01

The modeling of weather states (i.e., precipitation occurrences) is critical when the historical data are not long enough for the desired analysis. Stochastic models (e.g., Markov Chain and Alternating Renewal Process (ARP)) of the precipitation occurrence processes generally assume the existence of short-term temporal-dependency between the neighboring states while implying the existence of long-term independency (randomness) of states in precipitation records. Existing temporal-dependent models for the generation of precipitation occurrences are restricted either by the fixed-length memory (e.g., the order of a Markov chain model), or by the reining states in segments (e.g., persistency of homogenous states within dry/wet-spell lengths of an ARP). The modeling of variable segment lengths and states could be an arduous task and a flexible modeling approach is required for the preservation of various segmented patterns of precipitation data series. An innovative Dictionary approach has been developed in the field of genome pattern recognition for the identification of frequently occurring genome segments in DNA sequences. The genome segments delineate the biologically meaningful ``words" (i.e., segments with a specific patterns in a series of discrete states) that can be jointly modeled with variable lengths and states. A meaningful “word”, in hydrology, can be referred to a segment of precipitation occurrence comprising of wet or dry states. Such flexibility would provide a unique advantage over the traditional stochastic models for the generation of precipitation occurrences. Three stochastic models, namely, the alternating renewal process using Geometric distribution, the second-order Markov chain model, and the Dictionary approach have been assessed to evaluate their efficacy for the generation of daily precipitation sequences. Comparisons involved three guiding principles namely (i) the ability of models to preserve the short-term temporal-dependency in data through the concepts of autocorrelation, average mutual information, and Hurst exponent, (ii) the ability of models to preserve the persistency within the homogenous dry/wet weather states through analysis of dry/wet-spell lengths between the observed and generated data, and (iii) the ability to assesses the goodness-of-fit of models through the likelihood estimates (i.e., AIC and BIC). Past 30 years of observed daily precipitation records from 10 Canadian meteorological stations were utilized for comparative analyses of the three models. In general, the Markov chain model performed well. The remainders of the models were found to be competitive from one another depending upon the scope and purpose of the comparison. Although the Markov chain model has a certain advantage in the generation of daily precipitation occurrences, the structural flexibility offered by the Dictionary approach in modeling the varied segment lengths of heterogeneous weather states provides a distinct and powerful advantage in the generation of precipitation sequences.

Application of stochastic automata networks for creation of continuous time Markov chain models of voltage gating of gap junction channels.

PubMed

Snipas, Mindaugas; Pranevicius, Henrikas; Pranevicius, Mindaugas; Pranevicius, Osvaldas; Paulauskas, Nerijus; Bukauskas, Feliksas F

2015-01-01

The primary goal of this work was to study advantages of numerical methods used for the creation of continuous time Markov chain models (CTMC) of voltage gating of gap junction (GJ) channels composed of connexin protein. This task was accomplished by describing gating of GJs using the formalism of the stochastic automata networks (SANs), which allowed for very efficient building and storing of infinitesimal generator of the CTMC that allowed to produce matrices of the models containing a distinct block structure. All of that allowed us to develop efficient numerical methods for a steady-state solution of CTMC models. This allowed us to accelerate CPU time, which is necessary to solve CTMC models, ~20 times.

Neyman, Markov processes and survival analysis.

PubMed

Yang, Grace

2013-07-01

J. Neyman used stochastic processes extensively in his applied work. One example is the Fix and Neyman (F-N) competing risks model (1951) that uses finite homogeneous Markov processes to analyse clinical trials with breast cancer patients. We revisit the F-N model, and compare it with the Kaplan-Meier (K-M) formulation for right censored data. The comparison offers a way to generalize the K-M formulation to include risks of recovery and relapses in the calculation of a patient's survival probability. The generalization is to extend the F-N model to a nonhomogeneous Markov process. Closed-form solutions of the survival probability are available in special cases of the nonhomogeneous processes, like the popular multiple decrement model (including the K-M model) and Chiang's staging model, but these models do not consider recovery and relapses while the F-N model does. An analysis of sero-epidemiology current status data with recurrent events is illustrated. Fix and Neyman used Neyman's RBAN (regular best asymptotic normal) estimates for the risks, and provided a numerical example showing the importance of considering both the survival probability and the length of time of a patient living a normal life in the evaluation of clinical trials. The said extension would result in a complicated model and it is unlikely to find analytical closed-form solutions for survival analysis. With ever increasing computing power, numerical methods offer a viable way of investigating the problem.

Estimating parameters of hidden Markov models based on marked individuals: use of robust design data

USGS Publications Warehouse

Kendall, William L.; White, Gary C.; Hines, James E.; Langtimm, Catherine A.; Yoshizaki, Jun

2012-01-01

Development and use of multistate mark-recapture models, which provide estimates of parameters of Markov processes in the face of imperfect detection, have become common over the last twenty years. Recently, estimating parameters of hidden Markov models, where the state of an individual can be uncertain even when it is detected, has received attention. Previous work has shown that ignoring state uncertainty biases estimates of survival and state transition probabilities, thereby reducing the power to detect effects. Efforts to adjust for state uncertainty have included special cases and a general framework for a single sample per period of interest. We provide a flexible framework for adjusting for state uncertainty in multistate models, while utilizing multiple sampling occasions per period of interest to increase precision and remove parameter redundancy. These models also produce direct estimates of state structure for each primary period, even for the case where there is just one sampling occasion. We apply our model to expected value data, and to data from a study of Florida manatees, to provide examples of the improvement in precision due to secondary capture occasions. We also provide user-friendly software to implement these models. This general framework could also be used by practitioners to consider constrained models of particular interest, or model the relationship between within-primary period parameters (e.g., state structure) and between-primary period parameters (e.g., state transition probabilities).

Reliability modelling and analysis of a multi-state element based on a dynamic Bayesian network

PubMed Central

Xu, Tingxue; Gu, Junyuan; Dong, Qi; Fu, Linyu

2018-01-01

This paper presents a quantitative reliability modelling and analysis method for multi-state elements based on a combination of the Markov process and a dynamic Bayesian network (DBN), taking perfect repair, imperfect repair and condition-based maintenance (CBM) into consideration. The Markov models of elements without repair and under CBM are established, and an absorbing set is introduced to determine the reliability of the repairable element. According to the state-transition relations between the states determined by the Markov process, a DBN model is built. In addition, its parameters for series and parallel systems, namely, conditional probability tables, can be calculated by referring to the conditional degradation probabilities. Finally, the power of a control unit in a failure model is used as an example. A dynamic fault tree (DFT) is translated into a Bayesian network model, and subsequently extended to a DBN. The results show the state probabilities of an element and the system without repair, with perfect and imperfect repair, and under CBM, with an absorbing set plotted by differential equations and verified. Through referring forward, the reliability value of the control unit is determined in different kinds of modes. Finally, weak nodes are noted in the control unit. PMID:29765629

Susceptible-infected-susceptible epidemics on networks with general infection and cure times.

PubMed

Cator, E; van de Bovenkamp, R; Van Mieghem, P

2013-06-01

The classical, continuous-time susceptible-infected-susceptible (SIS) Markov epidemic model on an arbitrary network is extended to incorporate infection and curing or recovery times each characterized by a general distribution (rather than an exponential distribution as in Markov processes). This extension, called the generalized SIS (GSIS) model, is believed to have a much larger applicability to real-world epidemics (such as information spread in online social networks, real diseases, malware spread in computer networks, etc.) that likely do not feature exponential times. While the exact governing equations for the GSIS model are difficult to deduce due to their non-Markovian nature, accurate mean-field equations are derived that resemble our previous N-intertwined mean-field approximation (NIMFA) and so allow us to transfer the whole analytic machinery of the NIMFA to the GSIS model. In particular, we establish the criterion to compute the epidemic threshold in the GSIS model. Moreover, we show that the average number of infection attempts during a recovery time is the more natural key parameter, instead of the effective infection rate in the classical, continuous-time SIS Markov model. The relative simplicity of our mean-field results enables us to treat more general types of SIS epidemics, while offering an easier key parameter to measure the average activity of those general viral agents.

Multilayer Markov Random Field models for change detection in optical remote sensing images

NASA Astrophysics Data System (ADS)

Benedek, Csaba; Shadaydeh, Maha; Kato, Zoltan; Szirányi, Tamás; Zerubia, Josiane

2015-09-01

In this paper, we give a comparative study on three Multilayer Markov Random Field (MRF) based solutions proposed for change detection in optical remote sensing images, called Multicue MRF, Conditional Mixed Markov model, and Fusion MRF. Our purposes are twofold. On one hand, we highlight the significance of the focused model family and we set them against various state-of-the-art approaches through a thematic analysis and quantitative tests. We discuss the advantages and drawbacks of class comparison vs. direct approaches, usage of training data, various targeted application fields and different ways of Ground Truth generation, meantime informing the Reader in which roles the Multilayer MRFs can be efficiently applied. On the other hand we also emphasize the differences between the three focused models at various levels, considering the model structures, feature extraction, layer interpretation, change concept definition, parameter tuning and performance. We provide qualitative and quantitative comparison results using principally a publicly available change detection database which contains aerial image pairs and Ground Truth change masks. We conclude that the discussed models are competitive against alternative state-of-the-art solutions, if one uses them as pre-processing filters in multitemporal optical image analysis. In addition, they cover together a large range of applications, considering the different usage options of the three approaches.

An efficient interpolation technique for jump proposals in reversible-jump Markov chain Monte Carlo calculations

PubMed Central

Farr, W. M.; Mandel, I.; Stevens, D.

2015-01-01

Selection among alternative theoretical models given an observed dataset is an important challenge in many areas of physics and astronomy. Reversible-jump Markov chain Monte Carlo (RJMCMC) is an extremely powerful technique for performing Bayesian model selection, but it suffers from a fundamental difficulty and it requires jumps between model parameter spaces, but cannot efficiently explore both parameter spaces at once. Thus, a naive jump between parameter spaces is unlikely to be accepted in the Markov chain Monte Carlo (MCMC) algorithm and convergence is correspondingly slow. Here, we demonstrate an interpolation technique that uses samples from single-model MCMCs to propose intermodel jumps from an approximation to the single-model posterior of the target parameter space. The interpolation technique, based on a kD-tree data structure, is adaptive and efficient in modest dimensionality. We show that our technique leads to improved convergence over naive jumps in an RJMCMC, and compare it to other proposals in the literature to improve the convergence of RJMCMCs. We also demonstrate the use of the same interpolation technique as a way to construct efficient ‘global’ proposal distributions for single-model MCMCs without prior knowledge of the structure of the posterior distribution, and discuss improvements that permit the method to be used in higher dimensional spaces efficiently. PMID:26543580

Susceptible-infected-susceptible epidemics on networks with general infection and cure times

NASA Astrophysics Data System (ADS)

Cator, E.; van de Bovenkamp, R.; Van Mieghem, P.

2013-06-01

The classical, continuous-time susceptible-infected-susceptible (SIS) Markov epidemic model on an arbitrary network is extended to incorporate infection and curing or recovery times each characterized by a general distribution (rather than an exponential distribution as in Markov processes). This extension, called the generalized SIS (GSIS) model, is believed to have a much larger applicability to real-world epidemics (such as information spread in online social networks, real diseases, malware spread in computer networks, etc.) that likely do not feature exponential times. While the exact governing equations for the GSIS model are difficult to deduce due to their non-Markovian nature, accurate mean-field equations are derived that resemble our previous N-intertwined mean-field approximation (NIMFA) and so allow us to transfer the whole analytic machinery of the NIMFA to the GSIS model. In particular, we establish the criterion to compute the epidemic threshold in the GSIS model. Moreover, we show that the average number of infection attempts during a recovery time is the more natural key parameter, instead of the effective infection rate in the classical, continuous-time SIS Markov model. The relative simplicity of our mean-field results enables us to treat more general types of SIS epidemics, while offering an easier key parameter to measure the average activity of those general viral agents.

A novel grey-fuzzy-Markov and pattern recognition model for industrial accident forecasting

NASA Astrophysics Data System (ADS)

Edem, Inyeneobong Ekoi; Oke, Sunday Ayoola; Adebiyi, Kazeem Adekunle

2017-10-01

Industrial forecasting is a top-echelon research domain, which has over the past several years experienced highly provocative research discussions. The scope of this research domain continues to expand due to the continuous knowledge ignition motivated by scholars in the area. So, more intelligent and intellectual contributions on current research issues in the accident domain will potentially spark more lively academic, value-added discussions that will be of practical significance to members of the safety community. In this communication, a new grey-fuzzy-Markov time series model, developed from nondifferential grey interval analytical framework has been presented for the first time. This instrument forecasts future accident occurrences under time-invariance assumption. The actual contribution made in the article is to recognise accident occurrence patterns and decompose them into grey state principal pattern components. The architectural framework of the developed grey-fuzzy-Markov pattern recognition (GFMAPR) model has four stages: fuzzification, smoothening, defuzzification and whitenisation. The results of application of the developed novel model signify that forecasting could be effectively carried out under uncertain conditions and hence, positions the model as a distinctly superior tool for accident forecasting investigations. The novelty of the work lies in the capability of the model in making highly accurate predictions and forecasts based on the availability of small or incomplete accident data.

Inference for finite-sample trajectories in dynamic multi-state site-occupancy models using hidden Markov model smoothing

USGS Publications Warehouse

Fiske, Ian J.; Royle, J. Andrew; Gross, Kevin

2014-01-01

Ecologists and wildlife biologists increasingly use latent variable models to study patterns of species occurrence when detection is imperfect. These models have recently been generalized to accommodate both a more expansive description of state than simple presence or absence, and Markovian dynamics in the latent state over successive sampling seasons. In this paper, we write these multi-season, multi-state models as hidden Markov models to find both maximum likelihood estimates of model parameters and finite-sample estimators of the trajectory of the latent state over time. These estimators are especially useful for characterizing population trends in species of conservation concern. We also develop parametric bootstrap procedures that allow formal inference about latent trend. We examine model behavior through simulation, and we apply the model to data from the North American Amphibian Monitoring Program.