Coalescent Inference for Epidemics with Latent Periods

TL;DR

This paper develops a coalescent model for epidemics with latent periods, enabling inference of infected counts and reproduction numbers from genetic data, and introduces a Bayesian framework for analysis.

Contribution

It introduces a novel coalescent model for exposed-infected dynamics and a data-augmentation Bayesian inference method for epidemiological parameters.

Findings

Successfully applied to Ebola outbreak data

Accurately infers infection numbers over time

Performs well in simulation studies

Abstract

Coalescent models are used to study the transmission dynamics of rapidly evolving pathogens from molecular sequence data obtained from infected individuals. However coalescent parameters, such as effective population size, offer limited interpretability for transmission dynamics. In this work, we derive a coalescent model for exposed-infected population dynamics that allows us to infer the number of infected individuals and the effective reproduction number over time from the sample genealogy. The model can be interpreted as a two-deme model in which coalescence is restricted to individuals from different demes (exposed and infected). We propose a new data-augmentation framework with Phase-type distribution for Bayesian inference of epidemiological parameters. We study the performance of our approach on simulations and apply it to re-analyze the 2014 Ebola outbreak in Liberia.