NSF Award
2327751
All around the world, governments and communities have worked to control the spread of HIV/AIDS. However, despite dramatic progress, there are populations of individuals in which epidemic control has been difficult. One of these populations is composed of people who inject drugs (PWID). About one in five new HIV infections outside of Sub-Sharan Africa are in PWID, and in the US the number of PWID was estimated to be 3.7 million at the end of 2018. Hepatitis C (HCV) is another virus that has had a disproportionate impact on PWID. To eliminate or control HIV and HCV it is important to understand the factors that drive transmission and learn how best to intervene to prevent transmission of the viruses. These factors are difficult to identify among PWID because of the complex social networks of PWID, including their injection partners and the communities in which they live.<br/><br/>The goal of this work is to improve and develop interventions to control the spead of HIV and HCV in PWID. This project integrates behavioral, socioeconomic and mobility data with infectious disease transmission dynamics. The interdisciplinary research team brings together epidemiology, clinical medicine, behavioral science, engineering, and mathematical modeling. The project leverages large international datasets to develop complex transmission models and then uses machine learning and other novel data science techniques to fill in missing data and train the models. These models can be adapted to be used in any community to understand the factors that drive HIV and HCV transmission in their local PWID community.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
