Big-data analytics to develop a precision public health approach to HIV prevention and treatment in a hyperendemic rural African population

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Despite the successes and the implementation of population-wide HIV prevention and treatment efforts, HIV incidence rates remain unacceptably high in rural KwaZulu-Natal, South Africa, with the highest incidence peaks among young adults aged 20-30 years. While numerous individual and structural factors have been associated with the risk of HIV acquisition, traditional epidemiologic approaches do not sufficiently explain the totality of HIV acquisition risk. If HIV prevention efforts are to be successful, a comprehensive understanding of the underlying mechanisms for HIV risk and transmission will be critical. Our overarching goal in this proposal is to unravel the complex relationships and underlying mechanisms that place young adults at the highest risk of HIV acquisition, HIV transmission and treatment failure by harnessing heterogeneous population-level data and innovative big-data analytical approaches. The project will take advantage of one of the largest ongoing population-based HIV cohorts in the world - the Africa Health Research Institute’s population cohort in rural KwaZulu-Natal, with individual-level sociodemographic, biological, and clinical record data as well as comprehensive genomics data. The project will leverage the institute’s existing big data infrastructure as well as the recently established research platform for tracking individual mobility patterns via smartphones. Recent methodological innovations in machine-learning algorithms, smartphone-based geographic position system (GPS) tracking software applications, and viral gene sequencing technology provide an unparalleled opportunity to address key knowledge gaps to identify optimal strategies to prevent HIV transmission and improve HIV care in poor rural communities in sub-Saharan Africa. Specifically, the project will use the Africa Health Research Institute’s fully integrated individual data platform, smartphone-based GPS system and innovative big-data techniques: i) to elucidate the complex and interrelated factors that place young adults (20-30 years of age) at high risk of HIV acquisition; ii) to identify the constellation of factors that place HIV infected individuals at high risk of non-linkage to HIV care, treatment interruption, and viral non-suppression using machine learning algorithms; and iii) to design and pilot a smartphone intervention using a real-time and precision messaging system targeting those at high risk of acquisition of HIV infection, transmission, and treatment failure.