Accounting for contact network uncertainty in epidemic inferences

TL;DR

This paper introduces a novel Bayesian inference method that accounts for uncertainties in contact network data and epidemic observations, improving parameter estimation in infectious disease modeling.

Contribution

It proposes the MDN-ABC approach to incorporate contact network and epidemic data uncertainties into epidemic parameter inference.

Findings

Effective in simulated epidemic scenarios

Successfully applied to real dolphin disease data

Enhances accuracy of epidemic parameter estimates

Abstract

When modeling the dynamics of infectious disease, the incorporation of contact network information allows for the capture of the non-randomness and heterogeneity of realistic contact patterns. Oftentimes, it is assumed that the underlying contact pattern is known with perfect certainty. However, in realistic settings, the observed data often serves as an imperfect proxy of the actual contact patterns in the population. Furthermore, the epidemic in the real world are often not fully observed; event times such as infection and recovery times may be missing. In order to conduct accurate inferences on parameters of contagion spread, it is crucial to incorporate these sources of uncertainty. In this paper, we propose the use of Mixture Density Network compressed ABC (MDN-ABC) to learn informative summary statistics for the available data. This method will allow for Bayesian inference on the epidemic parameters of a contagious process, while accounting for imperfect observations on the epidemic and the contact network. We will demonstrate the use of this method on simulated epidemics and networks, and extend this framework to analyze the spread of Tattoo Skin Disease (TSD) among bottlenose dolphins in Shark Bay, Australia.