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The effective control of infectious diseases relies on accurate assessment ofthe impact of interventions, which is often hindered by the complex dynamics ofthe spread of disease. We propose a Beta-Dirichlet switching state-spacetransmission model to track underlying dynamics of disease and evaluate theeffectiveness of interventions simultaneously. As time evolves, the switchingmechanism introduced in the susceptible-exposed-infected-recovered (SEIR) modelis able to capture the timing and magnitude of changes in the transmission ratedue to the effectiveness of control measures. The implementation of this modelis based on a particle Markov Chain Monte Carlo algorithm, which can estimatethe time evolution of SEIR states, switching states, and high-dimensionalparameters efficiently. The efficacy of our model and estimation procedure aredemonstrated through simulation studies. With a real-world application toBritish 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 toinform public health policies aimed at studying the underlying dynamics andevaluating of the effectiveness of interventions during the spread of thedisease.

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