Understanding Transmission with integrated GENETIC and EPIDEMIOLOGIC inference
Dr. Eben Kenah (University of Florida) and Dr. Trevor Bedford (FHCRC) are junior investigators who are Co-Leaders of this research project.
The Investigators are based at Duke University, University of Michigan, University of Florida, FHCRC, and University of Washington.
The overall goal of this project is to develop, implement and validate novel methods to perform joint inference using combined epidemiologic and genetic data. This inference methodology seeks to provide estimates of fundamental transmission parameters, but also to provide estimates of unobserved transmission networks, virus genealogies, and time series of epidemiological state variables.
- To combine viral genetic data with epidemiologic data on person, place, and time to obtain efficient estimates of person-to-person transmission parameters. These sorts of datasets are commonly generated during transmission studies in households, schools, and similar settings, but also in analyses of novel outbreaks such as SARS or H7N9.
- To integrate coalescent theory with mechanistic models of population dynamics to improve inference from time series and genetic data. These sorts of broad-scale, but sparsely sampled, datasets are publicly available for a wide variety of pathogens, including influenza and dengue.
- To apply phylodynamic approaches developed in Aims 1 and 2 to viral sequence data to understand transmission structure and heterogeneity in influenza.