System inference via field inversion for the spatio-temporal progression of infectious diseases: Studies of COVID-19 in Michigan and Mexico

TL;DR

This paper introduces a PDE-based field inversion approach to model and predict the spatio-temporal spread of COVID-19, providing high-accuracy predictions and insights into infection dynamics and mobility patterns in Michigan and Mexico.

Contribution

It develops a novel PDE-based field inversion method using finite element spatial representation and constant parameters over time intervals for infectious disease modeling.

Findings

High-accuracy pandemic progression modeling in Michigan and Mexico

Effective short-term COVID-19 prediction into 2021

Revealed spatio-temporal variation of infection, recovery, and death rates

Abstract

We present an approach to studying and predicting the spatio-temporal progression of infectious diseases. We treat the problem by adopting a partial differential equation (PDE) version of the Susceptible, Infected, Recovered, Deceased (SIRD) compartmental model of epidemiology, which is achieved by replacing compartmental populations by their densities. Building on our recent work (Computational Mechanics, 66, 1177, 2020), we replace our earlier use of global polynomial basis functions with those having local support, as epitomized in the finite element method, for the spatial representation of the SIRD parameters. The time dependence is treated by inferring constant parameters over time intervals that coincide with the time step in semi-discrete numerical implementations. In combination, this amounts to a scheme of field inversion of the SIRD parameters over each time step. Applied to data over ten months of 2020 for the pandemic in the US state of Michigan and to all of Mexico, our system inference via field inversion infers spatio-temporally varying PDE SIRD parameters that replicate the progression of the pandemic with high accuracy. It also produces accurate predictions, when compared against data, for a three week period into 2021. Of note is the insight that is suggested on the spatio-temporal variation of infection, recovery and death rates, as well as patterns of the population's mobility revealed by diffusivities of the compartments.