A three-state coupled Markov switching model for COVID-19 outbreaks across Quebec based on hospital admissions

TL;DR

This paper introduces a Bayesian three-state coupled Markov switching model to analyze COVID-19 outbreaks across Quebec, incorporating spatial and covariate effects to improve outbreak detection and understanding.

Contribution

The model uniquely integrates coupled nonhomogeneous hidden Markov chains with covariate-dependent transition probabilities and clone states for better outbreak state estimation.

Findings

Mobility in retail and recreation venues positively correlates with outbreak development.

The model improves retrospective and real-time outbreak state estimation.

It allows estimation of covariate effects on disease extinction.

Abstract

Recurrent COVID-19 outbreaks have placed immense strain on the hospital system in Quebec. We develop a Bayesian three-state coupled Markov switching model to analyze COVID-19 outbreaks across Quebec based on admissions in the 30 largest hospitals. Within each catchment area, we assume the existence of three states for the disease: absence, a new state meant to account for many zeroes in some of the smaller areas, endemic and outbreak. Then we assume the disease switches between the three states in each area through a series of coupled nonhomogeneous hidden Markov chains. Unlike previous approaches, the transition probabilities may depend on covariates and the occurrence of outbreaks in neighboring areas, to account for geographical outbreak spread. Additionally, to prevent rapid switching between endemic and outbreak periods we introduce clone states into the model which enforce minimum endemic and outbreak durations. We make some interesting findings, such as that mobility in retail and recreation venues had a positive association with the development and persistence of new COVID-19 outbreaks in Quebec. Based on model comparison our contributions show promise in improving state estimation retrospectively and in real-time, especially when there are smaller areas and highly spatially synchronized outbreaks. Furthermore, our approach offers new and interesting epidemiological interpretations, such as being able to estimate the effect of covariates on disease extinction.