Bayesian MCMC for Ebola Outbreak Data

Epidemic records are full of holes: onset and removal times are partially observed and censored. This project develops a Bayesian data-augmentation MCMC analysis for Ebola outbreak data that treats the missing event times as latent variables and samples them jointly with the model parameters — recovering the transmission picture the raw records can’t show directly.