Universidad Autonoma de Santo Domingo, Dominican Republic-Community-Oriented Early Warning System

Overview

An AI-powered risk-prediction platform is changing how communities in high-risk regions detect, prepare for, and respond to mosquito-borne disease outbreaks before they happen.

In many low-resource settings, mosquito-borne diseases like malaria and dengue remain persistent public health threats, largely because outbreak detection comes too late. Communities lack real-time environmental monitoring tools, health workers have limited decision-support systems, and local populations often receive critical health information only after an outbreak has already taken hold.The platform addresses this gap by combining predictive analytics, real-time environmental monitoring, and AI-powered community engagement in a single integrated system. By analyzing mosquito trends, weather patterns, flood risks, and breeding site data, the platform generates accurate outbreak forecasts and early warnings. AI-powered health chatbots then translate these insights into simple, accessible alerts and preventive tips for local communities ensuring that the right information reaches the right people at the right time. The platform also invests in local capacity, training community leaders and health workers to interpret data and act quickly when risks are detected. The result is a proactive public health model that shifts communities from reactive crisis response to informed, early action strengthening preparedness, reducing outbreak severity, and saving lives. By embedding responsible AI practices including privacy safeguards, bias reduction, and close collaboration with local partners for cultural relevance the platform offers a scalable, community-driven blueprint for AI deployment in underserved health systems.