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Projecting COVID-19 cases and hospital burden in Ohio

https://doi.org/10.1016/j.jtbi.2022.111404

KhudaBukhsh, Wasiur R.; Bastian, Caleb Deen; Wascher, Matthew; Klaus, Colin; Sahai, Saumya Yashmohini; Weir, Mark H.; Kenah, Eben; Root, Elisabeth; Tien, Joseph H.; Rempała, Grzegorz A. (March 2023, Journal of Theoretical Biology)

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The effect of avoiding known infected neighbors on the persistence of a recurring infection process

https://doi.org/10.1214/22-EJP836

Chatterjee, Shirshendu; Sivakoff, David; Wascher, Matthew (January 2022, Electronic Journal of Probability)

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Assortative mixing among vaccination groups and biased estimation of reproduction numbers

https://doi.org/10.1016/S1473-3099(22)00155-4

Klaus, Colin; Wascher, Matthew; KhudaBukhsh, Wasiur R; Tien, Joseph H; Rempała, Grzegorz A; Kenah, Eben (May 2022, The Lancet Infectious Diseases)

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Likelihood-Free Dynamical Survival Analysis applied to the COVID-19 epidemic in Ohio

https://doi.org/10.3934/mbe.2023192

Klaus, Colin; Wascher, Matthew; KhudaBukhsh, Wasiur R.; Rempała, Grzegorz A. (January 2022, Mathematical Biosciences and Engineering)

The Dynamical Survival Analysis (DSA) is a framework for modeling epidemics based on mean field dynamics applied to individual (agent) level history of infection and recovery. Recently, the Dynamical Survival Analysis (DSA) method has been shown to be an effective tool in analyzing complex non-Markovian epidemic processes that are otherwise difficult to handle using standard methods. One of the advantages of Dynamical Survival Analysis (DSA) is its representation of typical epidemic data in a simple although not explicit form that involves solutions of certain differential equations. In this work we describe how a complex non-Markovian Dynamical Survival Analysis (DSA) model may be applied to a specific data set with the help of appropriate numerical and statistical schemes. The ideas are illustrated with a data example of the COVID-19 epidemic in Ohio.
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