Coupling Influenza Genetic and Epidemiological Data through Mathematical Modelling.

The first goal of the project is to use molecular data in conjunction with epidemiological data in order to reconstruct more accurate infection trees of outbreaks. We will base our analysis on the viral samples and epidemiological information collected by the Health Protection Agency during the H1N1v pandemic in the United Kingdom. We restrict our analysis to the early phase of the pandemic (from the first confirmed case on April 26th until the end of the first week in June 2009) during which 66 0 cases were confirmed. Out of these, 130 samples have been already sequenced but a substantial number of them are available for additional sequencing. Once the infection trees have been reconstructed by combining the available genetic and epidemiological data, we will select branches where chains of transmission are almost certain and use them to explore the way viral populations evolve through repeated transmissions. For this, we propose to do deep amplicon sequencing on the selected chains to see the extent of intra-host viral diversity and how this diversity is conserved during a transmission event. This information will be used to build an individual based model of transmission in the population incorporating the dynamics of viral evolution.