A Switching State-Space Transmission Model for Tracking Epidemics and Assessing Interventions

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. We propose a Beta-Dirichlet switching state-space transmission model 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 our model and estimation procedure are demonstrated through simulation studies. With a real-world application to British Columbia's COVID-19 outbreak, it indicates approximately a 66.6\% reduction of transmission rate following interventions such as distancing, closures and vaccination. Our proposed model provides a promising tool to inform public health policies aimed at studying the underlying dynamics and evaluating of the effectiveness of interventions during the spread of the disease.