The second of two reports I have written this year for STOR601 is on Bayesian inference for stochastic epidemics, supervised by Chris Jewell and Lloyd Chapman.
Specifically, the report focuses on the case when we do not have infection time data (a missing data problem). We introduce both the standard stochastic epidemic (SIR model) and Markov chain Monte Carlo (MCMC) methods, and then MCMC is applied to the SIR model. We also perform an empirical test, explore extensions to the method, and discuss open problems in the area.
View the report here: