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A data-driven Markov process for infectious disease transmission

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  • Chengliang Wang
  • Sohaib Mustafa

Abstract

The 2019 coronavirus pandemic exudes public health and socio-economic burden globally, raising an unprecedented concern for infectious diseases. Thus, describing the infectious disease transmission process to design effective intervention measures and restrict its spread is a critical scientific issue. We propose a level-dependent Markov model with infinite state space to characterize viral disorders like COVID-19. The levels and states in this model represent the stages of outbreak development and the possible number of infectious disease patients. The transfer of states between levels reflects the explosive transmission process of infectious disease. A simulation method with heterogeneous infection is proposed to solve the model rapidly. After that, simulation experiments were conducted using MATLAB according to the reported data on COVID-19 published by Johns Hopkins. Comparing the simulation results with the actual situation shows that our proposed model can well capture the transmission dynamics of infectious diseases with and without imposed interventions and evaluate the effectiveness of intervention strategies. Further, the influence of model parameters on transmission dynamics is analyzed, which helps to develop reasonable intervention strategies. The proposed approach extends the theoretical study of mathematical modeling of infectious diseases and contributes to developing models that can describe an infinite number of infected persons.

Suggested Citation

  • Chengliang Wang & Sohaib Mustafa, 2023. "A data-driven Markov process for infectious disease transmission," PLOS ONE, Public Library of Science, vol. 18(8), pages 1-20, August.
    • Handle: RePEc:plo:pone00:0289897
    DOI: 10.1371/journal.pone.0289897
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    References listed on IDEAS

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    1. Gourieroux, C. & Jasiak, J., 2023. "Time varying Markov process with partially observed aggregate data: An application to coronavirus," Journal of Econometrics, Elsevier, vol. 232(1), pages 35-51.
    2. Peter D. Lunn & Cameron A. Belton & Ciarán Lavin & Féidhlim P. McGowan & Shane Timmons & Deirdre A. Robertson, 2020. "Using behavioral science to help fight the Coronavirus," Journal of Behavioral Public Administration, Center for Experimental and Behavioral Public Administration, vol. 3(1).
    3. Samui, Piu & Mondal, Jayanta & Khajanchi, Subhas, 2020. "A mathematical model for COVID-19 transmission dynamics with a case study of India," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    4. Glenn Ellison, 2020. "Implications of Heterogeneous SIR Models for Analyses of COVID-19," NBER Working Papers 27373, National Bureau of Economic Research, Inc.
    5. Fabio Della Rossa & Davide Salzano & Anna Di Meglio & Francesco De Lellis & Marco Coraggio & Carmela Calabrese & Agostino Guarino & Ricardo Cardona-Rivera & Pietro De Lellis & Davide Liuzza & Francesc, 2020. "A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    6. Liang Wang & Xavier Didelot & Jing Yang & Gary Wong & Yi Shi & Wenjun Liu & George F. Gao & Yuhai Bi, 2020. "Inference of person-to-person transmission of COVID-19 reveals hidden super-spreading events during the early outbreak phase," Nature Communications, Nature, vol. 11(1), pages 1-6, December.
    7. Bera, Sovan & Khajanchi, Subhas & Roy, Tapan Kumar, 2022. "Dynamics of an HTLV-I infection model with delayed CTLs immune response," Applied Mathematics and Computation, Elsevier, vol. 430(C).
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