A novel framework for inferring parameters of transmission from viral sequence data

Abstract

Transmission between hosts is a critical part of the viral lifecycle. Recent studies of viral transmission have used genome sequence data to evaluate the number of particles transmitted between hosts, and the role of selection as it operates during the transmission process. However, the interpretation of sequence data describing transmission events is a challenging task. We here present a novel and comprehensive framework for using short-read sequence data to understand viral transmission events. Our model describes transmission as an event involving whole viruses, rather than independent alleles. We demonstrate how selection and noisy sequence data may each affect inferences of the population bottleneck, and identify circumstances in which selection for increased viral transmission may or may not be identified. Applying our model to data from a previous experimental transmission study, we show that our approach grants a more quantitative insight into viral transmission, inferring that between 2 to 6 viruses initiated infection, and allowing for a more informed interpretation of transmission events. While our model is here applied to influenza transmission, the framework we present is highly generalisable to other systems. Our work provides new opportunities for studying viral transmission.