  We develop a stochastic epidemic model progressing over dynamic networks,
where infection rates are heterogeneous and may vary with individual-level
covariates. The joint dynamics are modeled as a continuous-time Markov chain
such that disease transmission is constrained by the contact network structure,
and network evolution is in turn influenced by individual disease statuses. To
accommodate partial epidemic observations commonly seen in real-world data, we
propose a likelihood-based inference method based on the stochastic EM
algorithm, introducing key innovations that include efficient conditional
samplers for imputing missing infection and recovery times which respect the
dynamic contact network. Experiments on both synthetic and real datasets
demonstrate that our inference method can accurately and efficiently recover
model parameters and provide valuable insight at the presence of unobserved
disease episodes in epidemic data.
