System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19
Z. Wang, Xiaoxuan Zhang, Gregory H. Teichert, M. Carrasco-Teja, Krishna Garikipati
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.
Topics & Concepts
Coronavirus disease 2019 (COVID-19)InferenceBayesian inferencePandemicInfectious disease (medical specialty)Bayesian probability2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Epidemic modelPopulationEconometricsQuarantineStatistical inferenceStatistical physicsComputer scienceApplied mathematicsArtificial intelligenceMathematicsStatisticsVirologyBiologyDemographyDiseaseMedicinePhysicsOutbreakSociologyEcologyPathologyCOVID-19 epidemiological studiesBayesian Methods and Mixture ModelsStatistical Methods and Inference