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Female sex workers(FSWs) are one of the most vulnerable and stigmatizedgroups in society. As a result, they often suffer from a lack of quality accessto care. Grassroot organizations engaged in improving health services are oftenfaced with the challenge of improving the effectiveness of interventions due tocomplex influences. This work combines structure learning, discriminativemodeling, and grass-root level expertise of designing interventions across fivedifferent Indian states to discover the influence of non-obvious factors forimproving safe-sex practices in FSWs. A bootstrapped, ensemble-averagedBayesian Network structure was learned to quantify the factors that couldmaximize condom usage as revealed from the model. A discriminative model wasthen constructed using XgBoost and random forest in order to predict condom usebehavior The best model achieved 83% sensitivity, 99% specificity, and 99% areaunder the precision-recall curve for the prediction. Both generative anddiscriminative modeling approaches revealed that financial literacy trainingwas the primary influence and predictor of condom use in FSWs. These insightshave led to a currently ongoing field trial for assessing the real-worldutility of this approach. Our work highlights the potential of explainablemodels for transparent discovery and prioritization of anti-HIV interventionsin female sex workers in a resource-limited setting.

Learning Explainable Interventions to Mitigate HIV Transmission in Sex Workers Across Five States in India