A switching state-space transmission model for tracking epidemics and assessing interventions

TL;DR

This paper introduces a novel switching state-space model that dynamically tracks disease transmission and assesses intervention impacts, demonstrated through simulations and real COVID-19 data in British Columbia.

Contribution

It presents a Beta-Dirichlet switching state-space model with a particle MCMC algorithm for real-time epidemic tracking and intervention evaluation, advancing existing methods.

Findings

Model accurately captures changes in transmission rates.

Effectively estimates intervention impacts on disease spread.

Demonstrated success with COVID-19 data in British Columbia.

Abstract

The effective control of infectious diseases relies on accurate assessment of the impact of interventions, which is often hindered by the complex dynamics of the spread of disease. A Beta-Dirichlet switching state-space transmission model is proposed to track underlying dynamics of disease and evaluate the effectiveness of interventions simultaneously. As time evolves, the switching mechanism introduced in the susceptible-exposed-infected-recovered (SEIR) model is able to capture the timing and magnitude of changes in the transmission rate due to the effectiveness of control measures. The implementation of this model is based on a particle Markov Chain Monte Carlo algorithm, which can estimate the time evolution of SEIR states, switching states, and high-dimensional parameters efficiently. The efficacy of the proposed model and estimation procedure are demonstrated through simulation studies. With a real-world application to British Columbia's COVID-19 outbreak, the proposed switching state-space transmission model quantifies the reduction of transmission rate following interventions. The proposed model provides a promising tool to inform public health policies aimed at studying the underlying dynamics and evaluating the effectiveness of interventions during the spread of the disease.