Detecting Changes in the Transmission Rate of a Stochastic Epidemic Model

TL;DR

This paper introduces a likelihood-based method to detect and estimate changes in transmission rates of stochastic epidemic models, accounting for behavioral and policy-driven variations, using MCMC for change point detection.

Contribution

It presents a novel approach for jointly estimating transmission rate changes and change points in a stochastic SIR model with partial data, validated on real epidemic data.

Findings

Accurately detects change points in transmission rates.

Effectively estimates transmission parameters over time.

Demonstrates applicability on Ebola and COVID-19 data.

Abstract

Throughout the course of an epidemic, the rate at which disease spreads varies with behavioral changes, the emergence of new disease variants, and the introduction of mitigation policies. Estimating such changes in transmission rates can help us better model and predict the dynamics of an epidemic, and provide insight into the efficacy of control and intervention strategies. We present a method for likelihood-based estimation of parameters in the stochastic SIR model under a time-inhomogeneous transmission rate comprised of piecewise constant components. In doing so, our method simultaneously learns change points in the transmission rate via a Markov chain Monte Carlo algorithm. The method targets the exact model posterior in a difficult missing data setting given only partially observed case counts over time. We validate performance on simulated data before applying our approach to data from an Ebola outbreak in Western Africa and COVID-19 outbreak on a university campus.