A Predictive Model for One's Ability to Pay for Pre-Exposure Prophylaxis (PrEP) Services in Uganda

Overview

This project uses machine learning and AI models to identify, quantify, and map populations at high risk of HIV who are eligible for pre-exposure prophylaxis (PrEP). By generating data-driven insights on where and when to deploy services particularly among populations able to access and sustain PrEP the tool supports implementing partners in targeting interventions more effectively. Ultimately, it helps optimize resource allocation and improve the reach and impact of PrEP delivery.

Responsible AI Practices

The project leveraged a combination of publicly available and privately sourced datasets, ensuring contextual relevance