Predicting the Risk of Human Immunodeficiency Virus Type 1 (HIV-1) Acquisition in Rural South Africa Using Geospatial Data( )

BACKGROUND: Accurate human immunodeficiency virus (HIV) risk assessment can guide optimal HIV prevention. We evaluated the performance of risk prediction models incorporating geospatial measures. METHODS: We developed and validated HIV risk prediction models in a population-based cohort in South Africa. Individual-level covariates included demographic and sexual behavior measures, and geospatial covariates included community HIV prevalence and viral load estimates. We trained models on 2012–2015 data using LASSO Cox models and validated predictions in 2016–2019 data. We compared full models to simpler models restricted to only individual-level covariates or only age and geospatial covariates. We compared the spatial distribution of predicted risk to that of high incidence areas (≥ 3/100 person-years). RESULTS: Our analysis included 19 556 individuals contributing 44 871 person-years and 1308 seroconversions. Incidence among the highest predicted risk quintile using the full model was 6.6/100 person-years (women) and 2.8/100 person-years (men). Models using only age group and geospatial covariates had similar performance (women: AUROC = 0.65, men: AUROC = 0.71) to the full models (women: AUROC = 0.68, men: AUROC = 0.72). Geospatial models more accurately identified high incidence regions than individual-level models; 20% of the study area with the highest predicted risk accounted for 60% of the high incidence areas when using geospatial models but only 13% using models with only individual-level covariates. CONCLUSIONS: Geospatial models with no individual measures other than age group predicted HIV risk nearly as well as models that included detailed behavioral data. Geospatial models may help guide HIV prevention efforts to individuals and geographic areas at highest risk.