Grant: Fellowship - PA: Health Services/Population Health HIV/AIDS Research

The global HIV epidemic continues to outpace our response and the world health community is calling for a 'prevention revolution'. Key to our prevention response is an understanding of local HIV epidemics - how they begin and how they persist. But traditional methods of appraising epidemics do not take into account pathways of HIV transmission - that individuals with seemingly several degrees of separation can be linked by current or prior sexual partnerships. The links between people who participate in different sexual activity groups (for example, women or men who sell sex) and their partners' partners and individuals without any apparent direct sexual connection to a higher-risk network, are key to understanding how HIV may spread within a community and how prevention efforts along the pathways can impact on the epidemic as a whole. We recently showed that when applied to Indian districts, results of traditional epidemic appraisals developed by UNAIDS did not coincide with the prevention strategies already in place - strategies developed by local public health teams and community based organizations who understood the sexual structure of the population. We therefore proposed a modified framework based on key transmission pathways observed in India. But how does this framework compare with the traditional methods, and how can it be used rapidly and reliably by policy-makers? We aim to address these questions with the use of mathematical models to simulate observed epidemics at a district-level in India. We will test the short and long-term impact of prevention strategies allocated based on traditional epidemic appraisals as compared with the modified framework. We will then determine the key features of a district that help distinguish local epidemics within the framework so districts may be reliably classified based on where and how prevention resources should be allocated to not only prevent infections in the short-term, but also control the epidemic.