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Updated: Jul 24, 2026

Vaccinia Virus Infection & Temporal Analysis of Virus Gene Expression: Part 1
Published on: April 8, 2009
System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19
Z Wang1, X Zhang1, G H Teichert1
1Mechanical Engineering, Mathematics and the Michigan Institute for Computational, Discovery and Engineering, University of Michigan, Ann Arbor, USA.
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. in Comput Methods Appl Mech Eng 356:44-74, 2019; arXiv:2001.04816 [cs.CE]) as well as Bayesian machine learning methods.
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