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

TL;DR

This review evaluates methods for predicting and warning about dengue outbreaks, comparing models and systems used globally.

Contribution

The study systematically categorizes and compares prediction and warning systems for dengue, highlighting performance and limitations.

Findings

Meteorological and climatic variables are most commonly used in dengue prediction models.

Random Forest and LSTM models show superior performance for short-term forecasts.

Integrated warning systems like EWARS-TDR provide longer lead times but face implementation challenges.

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.

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