Targeted Learning using Adaptive Designs for HIV Epidemic Control in East Africa

In the current HIV epidemic response, high variability in implementing contexts and epidemic settings demands epidemiological designs and analytic methods that are able to detect and respond to heterogeneity effectively and efficiently. This project leverages the rich data increasingly generated in the course of the HIV epidemic response, and applies targeted machine learning to advance adaptive design and analytic approaches.