Abstract

Understanding the transmission of infectious disease is important for monitoring outbreaks, informing public health policy, and improving intervention strategies. Traditionally the fields of population genetics and epidemiology have been studied separately; however it is clear that using genetic information alongside epidemiological models has great potential for understanding the dynamics of infectious disease. Directly estimating epidemiological parameters such as transmission rates can be difficult, as it relies on comprehensive monitoring during an outbreak where relevant processes may be hidden or undetectable. However, genetic information provides an alternative window into the past. I will talk about a combined coalescent-based meta-population model for estimating the parameters of standard SI, SIS and SIR epidemiological models from genetic data. I will apply these models to a meta-analysis of Hepatitis C virus (HCV), with the aim of explaining differences in patterns of genetic diversity between populations in terms of the underlying epidemiological dynamics. I will look at differences between datasets in the growth rate of HCV and whether they are explained by subtype, host population size or prevalence of disease to understand the factors that drive global variation in Hepatitis C diversity.