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Bayesian inference of a spatially dependent semi-Markovian model with application to Madagascar Covid’19 data

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  • Angelo Raherinirina
  • Stefana Tabera Tsilefa
  • Tsidikaina Nirilanto
  • Solym M Manou-Abi

Abstract

This article presents an approach to stochastic analysis of disease dynamics. We develop an explicit semi-Markovian model that accounts for spatial dependence, operating in discrete time over a finite state space. The model allowed us to have a propagation model conditioned by neighboring states and quantifies two key characteristics : spatial propagation timescales and propagation law in a region dependent on neighboring states. The model is inferred from data collected on the spread of Covid’19 in Madagascar’s 22 regions, using the Bayesian approach to get a better idea of model parameter values. The result has demonstrated the effect of neighborhoods on the propagation dynamics of diseases. We conclude with a discussion of potential future theoretical developments.

Suggested Citation

  • Angelo Raherinirina & Stefana Tabera Tsilefa & Tsidikaina Nirilanto & Solym M Manou-Abi, 2025. "Bayesian inference of a spatially dependent semi-Markovian model with application to Madagascar Covid’19 data," PLOS ONE, Public Library of Science, vol. 20(7), pages 1-25, July.
    • Handle: RePEc:plo:pone00:0326264
    DOI: 10.1371/journal.pone.0326264
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    1. repec:dau:papers:123456789/1906 is not listed on IDEAS
    2. Daniel Silver & Thiago H Silva, 2021. "A Markov model of urban evolution: Neighbourhood change as a complex process," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-29, January.
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