Leveraging data science to uncover solutions to air pollution-related adverse health outcomes in Africa: policy advice and recommendations

Abstract

Air pollution is a significant risk factor for a wide range of adverse health outcomes globally. In Africa, the health burden is exacerbated by high levels of pollution, limited infrastructure, and restricted access to health care interventions and solutions. Data science presents a valuable opportunity to address these challenges through enhanced prediction, monitoring, and response strategies. This scoping review provides a comprehensive analysis of data science applications in air pollution and health research across Africa. We examine how data science approaches such as machine learning, geospatial analysis, and predictive modelling are being employed to strengthen climate change forecasting and guide public health interventions. By synthesizing evidence from diverse African contexts, this study highlights the transformative potential of data science to inform evidence-based, context-specific responses to air pollution and its health impacts on the continent.