Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact data
Reconstructing the history of transmission events in an infectious disease outbreak provides valuable information for informing infection control policy. Recent years have seen considerable progress in the development of statistical tools for the inference of such transmission trees from outbreak data, with a major focus on whole genome sequence data (WGS). However, complex evolutionary behavior, missing sequences and the limited diversity accumulating along transmission chains limit the power of existing approaches in reconstructing outbreaks. We have developed a methodology that uses information on the contact structures between cases to infer likely transmission links, alongside genomic and temporal data. Such contact data is frequently collected in outbreak settings, for example during Ebola, HIV or Tuberculosis outbreaks, and can be highly informative of the infectious relationships between cases. Using simulations, we show that our contact model effectively incorporates this information and improves the accuracy of outbreak reconstruction even when only a portion of contacts are reported. We then apply our method to the 2003 SARS outbreak in Singapore and describe the range of transmission scenarios consistent with genetic data and contact data for the first time. Our work suggests that, whenever available, contact data should be explicitly incorporated in outbreak reconstruction tools.