Forecasting and Early Warning Systems for Dengue Outbreaks: Updated Narrative Review

Abstract

In this review, we examine dengue outbreak prediction and warning systems, highlighting their methodologies, variables, key findings, and existing gaps in the literature. The study was conducted in five stages: a literature survey, definition of thematic scope and eligibility criteria, exploratory review, systematization and categorization of findings, critical analysis, and comparative narrative synthesis. We selected 14 articles on prediction and seven on warning systems, encompassing statistical models, machine learning, and deep learning, as well as systems applied in various countries, with a particular focus on Brazil. The results indicated that meteorological and climatic variables are the most frequently used, followed by epidemiological and entomological data. Models such as Random Forest and Long Short-Term Memory demonstrated superior predictive performance, especially for short-term forecasts of up to 1 week. Among the warning systems, classical methods, such as the Early Aberration Reporting System, offer simplicity and speed but provide shorter lead times. In contrast, systems such as EWARS-TDR and ADSEWS excel by integrating multiple data sources and providing longer lead times (up to 13 weeks). Despite considerable advancements, challenges related to data quality and availability, model replicability across different contexts, and implementation persist in public health systems.