  In this paper, we propose a Susceptible-Infected-Removal (SIR) model with
time fused coefficients. In particular, our proposed model discovers the
underlying time homogeneity pattern for the SIR model's transmission rate and
removal rate via Bayesian shrinkage priors. MCMC sampling for the proposed
method is facilitated by the nimble package in R. Extensive simulation studies
are carried out to examine the empirical performance of the proposed methods.
We further apply the proposed methodology to analyze different levels of
COVID-19 data in the United States.
