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arXiv · 2007.00865

System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19

Zhenlin Wang·Xiaoxuan Zhang·Gregory Teichert·Mariana Carrasco-Teja·Krishna Garikipati
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Abstract

We extend the classical SIR model of infectious disease spread to account for time dependence in the parameters, which also include diffusivities. The temporal dependence accounts for the changing characteristics of testing, quarantine and treatment protocols, while diffusivity incorporates a mobile population. This model has been applied to data on the evolution of the COVID-19 pandemic in the US state of Michigan. For system inference, we use recent advances; specifically our framework for Variational System Identification (Wang et al., Comp. Meth. App. Mech. Eng., 356, 44-74, 2019; arXiv:2001.04816 [cs.CE]) as well as Bayesian machine learning methods.

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Zhenlin Wang, Xiaoxuan Zhang, Gregory Teichert, Mariana Carrasco-Teja, Krishna Garikipati. 2020-07-02. System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19. https://arxiv.org/abs/2007.00865

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