Likelihood-based inference for partially observed stochastic epidemics with individual heterogeneity

TL;DR

This paper introduces a likelihood-based inference method for stochastic epidemic models on dynamic networks with individual heterogeneity, capable of handling partial observations and accurately estimating parameters.

Contribution

It presents a novel stochastic EM algorithm with efficient samplers for missing data in dynamic contact networks, advancing epidemic inference methods.

Findings

Accurately recovers model parameters from synthetic data.

Effectively infers unobserved disease episodes in real datasets.

Demonstrates computational efficiency and robustness.

Abstract

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.