ABSTRACT/SUMMARY HIV incidence is declining in sub-Saharan Africa (SSA), but some areas are not on track to achieve the “epidemic control” target of <0.1% annual rate of new infections set by the United Nations in the Sustainable Development Goals. Common mental disorders (CMD) such as depression and anxiety are extremely prevalent among people living with HIV, interfere with lifelong adherence to treatment, and are associated with risky sexual behaviors. Accordingly, health authorities in SSA are recognizing the importance of CMD screening and treatment as a component of HIV prevention, but have not yet determined where to focus these services. The areas at greatest risk of missing the epidemic control target are known as HIV hotspots. We hypothesize that there are different types of hotspots: some driven by risky behaviors, some arising by random chance, and some that emerged in the past and have become trapped in a positive feedback loop between incidence and prevalence. The optimal role of CMD screening and treatment may differ by hotspot type. We propose to use mathematical modeling to predict the occurrence of hotspots, determine how to classify them by type, and estimate the optimal role of CMD screening and treatment for each HIV hotspot type. Next, we will work with experts in two SSA countries – Kenya and Zambia – to develop an investment case for incorporating CMD screening and treatment into an optimal HIV response, either nationally or with a focus on HIV hotspots. Finally, we will use a value-of-information approach to determine whether the costs of finding and classifying hotspots are justified by the benefits of targeting and customizing the HIV response to each hotspot. We expect our project to guide Ministries of Health and implementers about scale-up of CMD treatment in SSA while also expanding basic understanding of HIV epidemics, which were hypothesized to contain hotspots due to fundamental mathematical properties such as fractals and chaos. These hypotheses have not been revisited in two decades, while geospatial HIV data and methods have proliferated. We expect that our project will enable more effective HIV prevention in SSA by expanding hotspot targeting and by facilitating the integration of CMD care into the HIV response.