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Bayesian space-time SIR modeling of Covid-19 in two US states during the 2020–2021 pandemic

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  • Andrew B Lawson
  • Joanne Kim

Abstract

This paper describes the Bayesian SIR modeling of the 3 waves of Covid-19 in two contrasting US states during 2020–2021. A variety of models are evaluated at the county level for goodness-of-fit and an assessment of confounding predictors is also made. It is found that models with three deprivation predictors and neighborhood effects are important. In addition, the work index from Google mobility was also found to provide an increased explanation of the transmission dynamics.

Suggested Citation

  • Andrew B Lawson & Joanne Kim, 2022. "Bayesian space-time SIR modeling of Covid-19 in two US states during the 2020–2021 pandemic," PLOS ONE, Public Library of Science, vol. 17(12), pages 1-20, December.
    • Handle: RePEc:plo:pone00:0278515
    DOI: 10.1371/journal.pone.0278515
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    References listed on IDEAS

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    1. Alexander Morton & Bärbel F. Finkenstädt, 2005. "Discrete time modelling of disease incidence time series by using Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 575-594, June.
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