Predicting the spatiotemporal evolution of HIV/AIDS in Africa: A retrospective analysis of epidemiological trends

Background Africa bears the highest global burden of HIV, with marked regional inequalities in prevalence, incidence and clinical outcomes. Mapping the spatial and temporal evolution of the epidemic is essential to guide targeted interventions and anticipate future trends. Methods We conducted a retrospective analysis of UNAIDS annual estimates for adults aged 15–49 years across 49 African countries (2014–2023). We described spatiotemporal patterns in HIV prevalence, incidence, adults living with HIV (ALHIV) and AIDS-related deaths, and quantified temporal trends using annual percentage change and linear regression. For the ten highest-burden countries, we forecast prevalence to 2033 using an ensemble of machine learning models. Hierarchical and k-means clustering, supported by principal component analysis, were applied to identify epidemic archetypes based on average prevalence levels and temporal trajectories. Results Southern Africa remained the epicentre of the epidemic, with mean adult prevalence of 19.97% versus <1.3% in Northern and Western Africa. From 2014 to 2023, prevalence and incidence declined in all regions, with the steepest reductions in Southern (prevalence −19.5%; incidence −68.4%) and Eastern Africa (−22.2% and −65.6%, respectively). Despite falling rates, the absolute number of ALHIV increased in several regions, while AIDS-related deaths decreased by more than 44% in Central and Western Africa. Forecasts for the highest-burden countries indicate a continued, gradual decline in prevalence. Cluster analysis identified a hyperendemic group of six Southern African countries (mean prevalence 15.6%) and a second cluster of 41 countries with moderate-to-low prevalence (2.1%) and mainly stable or declining trajectories. Conclusions The African HIV epidemic is increasingly heterogeneous and evolving rather than uniformly controlled. Combining machine learning forecasts and clustering with routine surveillance can support differentiated, data-driven strategies that intensify prevention and treatment in hyperendemic settings while sustaining gains elsewhere.