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Hierarchical Bayesian spatio-temporal modeling of COVID-19 in the United States

Author

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  • Kevin D. Dayaratna
  • Drew Gonshorowski
  • Mary Kolesar

Abstract

We examine the impact of economic, demographic, and mobility-related factors have had on the transmission of COVID-19 in 2020. While many models in the academic literature employ linear/generalized linear models, few contributions exist that incorporate spatial analysis, which is useful for understanding factors influencing the proliferation of the disease before the introduction of vaccines. We utilize a Poisson generalized linear model coupled with a spatial autoregressive structure to do so. Our analysis yields a number of insights including that, in some areas of the country, the counterintuitive result that staying at home can lead to increased disease proliferation. Additionally, we find some positive effects from increased gathering at grocery stores, negative effects of visiting retail stores and workplaces, and even small effects on visiting parks highlighting the complexities travel and migration have on the transmission of diseases.

Suggested Citation

  • Kevin D. Dayaratna & Drew Gonshorowski & Mary Kolesar, 2023. "Hierarchical Bayesian spatio-temporal modeling of COVID-19 in the United States," Journal of Applied Statistics, Taylor & Francis Journals, vol. 50(11-12), pages 2663-2680, September.
    • Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2663-2680
    DOI: 10.1080/02664763.2022.2069232
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