Identifying and Forecasting Zika Hot Spots by Finding the Data Cold Spot

This project will leverage one of the most widely used Frontline Worker systems, CommCare, and cutting edge geospatial and predictive algorithms from ATLAS to identify cold spots and compute their risks for different diseases across Latin America. This information can be acted upon to gather more information to detect and manage disease outbreaks. The program will combine three key elements: real-time geo-coded data from front-line workers who are using CommCare, automated population estimates from satellite image analysis through machine learning, and data-driven Zika risk indices inferred from additional spatial and health data such as density of pregnant women, bodies of standing water, air temperature, and humidity.