Our overall objective is to develop detailed spatially-explicit data-based HIV transmission dynamic models and to use these models to design optimal interventions for preventing HIV infections. The research we are proposing builds upon our past two decades of research on modeling HIV and other infectious diseases, but takes our research in a new direction by: (i) focusing on building detailed data-based spatially-explicit models and (ii) using optimization techniques to design HIV interventions. In addition our proposed new research direction involves new collaborations: specifically with Columbia University, the Center for Disease Control and Prevention (CDC) and the African Comprehensive HIV and AIDS Program (ACHAP);ACHAP is a collaboration between the Government of Botswana and the Bill and Melinda Gates Foundation.
We aim to design optimal rollout plans for interventions based on male circumcision (MC) and pre exposure prophylaxis (PrEP) in Botswana using data-based models. Botswana has one of the highest levels of HIV worldwide;~40% of its'730,000 adults between the ages of 15 &49 are infected [1]. By constructing detailed spatially-explicit transmission models and employing mathematical optimization techniques we will be able to show how to maximize the effectiveness of MC and PrEP interventions. Specifically, we will identify the geographic locations where the rollout of these interventions should begin, and determine the gender (for PrEP) and age-groups (for PrEP and MC) that should be targeted. By developing high resolution data-based spatial risk maps we will be able to make realistic predictions for the potential epidemiological impact of MC (and PrEP) on incidence (and drug resistance) in Botswana. In addition the risk maps will enable us to assess the feasibility of the Governments'MC rollout plan. Our methods and results will be able to be used to determine and evaluate critical health policy decisions regarding MC and PrEP interventions in Botswana and other African countries.
We will develop detailed spatially-explicit data-based HIV transmission dynamic models and use these models to design optimal interventions for preventing HIV infections. Our methods and results will be able to be used to determine and evaluate critical health policy decisions regarding MC and PrEP interventions in Botswana, as well as in other African countries. Therefore our results will have direct relevance for health policy makers and Governments in Africa, as well as for other scientists.
