Enhancing Bayesian risk prediction for epidemics using contact tracing

TL;DR

This paper introduces a Bayesian method that combines contact tracing and contact network data to enhance epidemic risk prediction, demonstrating improved accuracy and control strategies in a simulated poultry industry outbreak.

Contribution

It develops a novel Bayesian inference approach that effectively integrates contact tracing data, even when incomplete, to improve epidemic modeling and prediction.

Findings

Contact tracing data improves posterior predictive accuracy.

The method informs more effective epidemic control strategies.

Robustness to partial contact tracing is demonstrated.

Abstract

Contact tracing data collected from disease outbreaks has received relatively little attention in the epidemic modelling literature because it is thought to be unreliable: infection sources might be wrongly attributed, or data might be missing due to resource contraints in the questionnaire exercise. Nevertheless, these data might provide a rich source of information on disease transmission rate. This paper presents novel methodology for combining contact tracing data with rate-based contact network data to improve posterior precision, and therefore predictive accuracy. We present an advancement in Bayesian inference for epidemics that assimilates these data, and is robust to partial contact tracing. Using a simulation study based on the British poultry industry, we show how the presence of contact tracing data improves posterior predictive accuracy, and can directly inform a more effective control strategy.