  Throughout the course of an epidemic, the rate at which disease spreads
varies with behavioral changes, the emergence of new disease variants, and the
introduction of mitigation policies. Estimating such changes in transmission
rates can help us better model and predict the dynamics of an epidemic, and
provide insight into the efficacy of control and intervention strategies. We
present a method for likelihood-based estimation of parameters in the
stochastic SIR model under a time-inhomogeneous transmission rate comprised of
piecewise constant components. In doing so, our method simultaneously learns
change points in the transmission rate via a Markov chain Monte Carlo
algorithm. The method targets the exact model posterior in a difficult missing
data setting given only partially observed case counts over time. We validate
performance on simulated data before applying our approach to data from an
Ebola outbreak in Western Africa and COVID-19 outbreak on a university campus.
