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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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