Enhancing Bayesian risk prediction for epidemics using contact tracing

Contact-tracing data (CTD) collected from disease outbreaks has received relatively little attention in the epidemic modeling literature because it is thought to be unreliable: infection sources might be wrongly attributed, or data might be missing due to resource constraints in the questionnaire exercise.Nevertheless, these data might provide a rich source of information on the disease transmission rate.This paper presents a novel methodology for combining CTD 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 CTD improves posterior predictive accuracy and can directly inform a more effective control strategy.