  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.
