Webinar Details
Speaker: Omar Aristides Ureña Medina
Language: Spanish
Date: Thursday, August 20, 2026
Time: 12:00 – 1:00pm. EDT
Topic: Early Prediction of Dengue Outbreaks in the Dominican Republic Using Machine Learning Models, Climatic Variables, and Digital Search Trends
Zoom: Register for the webinar via Zoom
Abstract
We built a machine learning system to detect early dengue outbreaks in the Dominican Republic, drawing on weekly surveillance data from 2006 to 2024. The model integrates epidemiological, meteorological, sea surface temperature, and Google Trends data. We labeled outbreaks with the Farrington Flexible algorithm and trained regression and classification models using expanding-window cross-validation. Our classification models reached up to 90% sensitivity, showing how data-driven forecasting can strengthen surveillance and enable faster public health responses.
Bio
Omar Ureña is a final-year medical student at the Universidad Autónoma de Santo Domingo (UASD) in the Dominican Republic. He started programming at 13 and now applies artificial intelligence and data science to epidemiology and public health. His work spans predictive modeling for infectious diseases, suicide epidemiology, and AI-driven clinical decision support.