A stochastic metapopulation state-space approach to modeling and estimating Covid-19 spread

TL;DR

This paper introduces a stochastic metapopulation state-space model for COVID-19 that estimates hidden states and parameters from noisy data using advanced filtering and optimization techniques, demonstrated on synthetic and real data.

Contribution

It presents a novel stochastic SEIRD model with a state-space framework for COVID-19, enabling estimation from incomplete data with advanced filtering methods.

Findings

Effective estimation of hidden states and parameters from noisy data

Successful application to synthetic and real COVID-19 data

Demonstrates model's potential for public health decision support

Abstract

Mathematical models are widely recognized as an important tool for analyzing and understanding the dynamics of infectious disease outbreaks, predict their future trends, and evaluate public health intervention measures for disease control and elimination. We propose a novel stochastic metapopulation state-space model for COVID-19 transmission, based on a discrete-time spatio-temporal susceptible/exposed/infected/recovered/deceased (SEIRD) model. The proposed framework allows the hidden SEIRD states and unknown transmission parameters to be estimated from noisy, incomplete time series of reported epidemiological data, by application of unscented Kalman filtering (UKF), maximum-likelihood adaptive filtering, and metaheuristic optimization. Experiments using both synthetic data and real data from the Fall 2020 Covid-19 wave in the state of Texas demonstrate the effectiveness of the proposed model.