Harnessing Data Science and Analytics to Strengthen Maternal, Newborn and Child Health Monitoring and Eliminate HIV Transmission in Low-Resource Settings

Christopher Seebregts of Jembi Health Systems NPC in South Africa will use a data science approach to improve maternal, newborn, and child health by developing algorithms that integrate diverse personal and clinical data taken from disparate sources to make them more informative. To test their approach, they will apply it to existing data taken in the Tshwane health district in South Africa for a program looking to prevent mother-to-child transmission of HIV. These data include quantitative diagnostic readouts on HIV status for mother and child, therapy initiation reports, household and socio-economic data, and district health information, all produced by different groups using different formats. They will use data science methodology and algorithms to integrate all the data to produce longitudinal and cohort data and apply machine learning techniques to identify predictors of failure to prevent mother-to-child HIV transmission, and of mother or infant mortality.