Understanding HIV transmission using epidemiological data and mathematical modeling

In this project you will use state-of-the-art viral sequencing data, combined with epidemiological data and mathematical modeling, to create an integrated understanding of HIV transmission. HIV places an enormous burden on global health. Implementing treatment and interventions can save millions of lives, but to do this effectively requires us to be able to predict the outcome of interventions, and to be able to accurately assess how well they are working once implemented. For HIV, these efforts are hampered by long durations of infections, and rapid within-host viral evolution during infection, meaning the virus an individual is infected with is unlikely to be the same as any viruses they go on to transmit.
 
For this project, you will identify individuals enrolled in the Rakai Community Cohort Project, based in Uganda, who are part of possible transmission chains, and for whom multiple blood samples are available throughout infection and at the time of transmission. These samples will be sequenced using state-of-the-art technology developed at the University of Oxford enabling the sequencing of thousands of whole virus genomes per sample, without the need to break the viral genomes into short fragments (whole-haplotype deep sequencing). Using this data, you will comprehensively characterize viral diversity during infection and at the point of transmission.

Key questions you will tackle are:
 
- Do ‘founder-like’ viruses (similar to those that initiated infection) persist during chronic infection?
- Is there a consistent pattern of evolution towards population consensus virus?
- Are ‘founder-like’ viruses, or ‘consensus-like’ viruses more likely to be transmitted?
- Does the transmission of drug-resistant virus depend on the history of the transmitting partner?

*This project is available for the 2021 Oxford-NIH Pilot Programme*