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Generalized Markov models of infectious disease spread: A novel framework for developing dynamic health policies

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  • Yaesoubi, Reza
  • Cohen, Ted

Abstract

We propose a class of mathematical models for the transmission of infectious diseases in large populations. This class of models, which generalizes the existing discrete-time Markov chain models of infectious diseases, is compatible with efficient dynamic optimization techniques to assist real-time selection and modification of public health interventions in response to evolving epidemiological situations and changing availability of information and medical resources. While retaining the strength of existing classes of mathematical models in their ability to represent the within-host natural history of disease and between-host transmission dynamics, the proposed models possess two advantages over previous models: (1) these models can be used to generate optimal dynamic health policies for controlling spreads of infectious diseases, and (2) these models are able to approximate the spread of the disease in relatively large populations with a limited state space size and computation time.

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  • Yaesoubi, Reza & Cohen, Ted, 2011. "Generalized Markov models of infectious disease spread: A novel framework for developing dynamic health policies," European Journal of Operational Research, Elsevier, vol. 215(3), pages 679-687, December.
    • Handle: RePEc:eee:ejores:v:215:y:2011:i:3:p:679-687
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    References listed on IDEAS

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    Cited by:

    1. Christoph Zimmer & Reza Yaesoubi & Ted Cohen, 2017. "A Likelihood Approach for Real-Time Calibration of Stochastic Compartmental Epidemic Models," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-21, January.
    2. De Angelis, Luca & Dias, José G., 2014. "Mining categorical sequences from data using a hybrid clustering method," European Journal of Operational Research, Elsevier, vol. 234(3), pages 720-730.
    3. Das, Saikat & Bose, Indranil & Sarkar, Uttam Kumar, 2023. "Predicting the outbreak of epidemics using a network-based approach," European Journal of Operational Research, Elsevier, vol. 309(2), pages 819-831.
    4. Serife Yilmaz & Ekaterina Dudkina & Michelangelo Bin & Emanuele Crisostomi & Pietro Ferraro & Roderick Murray-Smith & Thomas Parisini & Lewi Stone & Robert Shorten, 2020. "Kemeny-based testing for COVID-19," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-19, November.
    5. Yunhan Huang & Quanyan Zhu, 2022. "Game-Theoretic Frameworks for Epidemic Spreading and Human Decision-Making: A Review," Dynamic Games and Applications, Springer, vol. 12(1), pages 7-48, March.
    6. Dimitrov, Nedialko B. & Dimitrov, Stanko & Chukova, Stefanka, 2014. "Robust decomposable Markov decision processes motivated by allocating school budgets," European Journal of Operational Research, Elsevier, vol. 239(1), pages 199-213.
    7. Lorena Pradenas & Marco Fuentes & Víctor Parada, 2020. "Optimizing waste storage areas in health care centers," Annals of Operations Research, Springer, vol. 295(1), pages 503-516, December.
    8. Se Yoon Lee & Bowen Lei & Bani Mallick, 2020. "Estimation of COVID-19 spread curves integrating global data and borrowing information," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-17, July.
    9. Pietro Hiram Guzzi & Giuseppe Tradigo & Pierangelo Veltri, 2020. "Spatio-Temporal Resource Mapping for Intensive Care Units at Regional Level for COVID-19 Emergency in Italy," IJERPH, MDPI, vol. 17(10), pages 1-9, May.
    10. Anne-France Viet & Stéphane Krebs & Olivier Rat-Aspert & Laurent Jeanpierre & Catherine Belloc & Pauline Ezanno, 2018. "A modelling framework based on MDP to coordinate farmers' disease control decisions at a regional scale," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-20, June.
    11. Allen, D.E. & Powell, R.J. & Singh, A.K., 2016. "Take it to the limit: Innovative CVaR applications to extreme credit risk measurement," European Journal of Operational Research, Elsevier, vol. 249(2), pages 465-475.

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