ArticlePLoS neglected tropical diseases2020
Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia.
Article in PLoS neglected tropical diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 3 of them syntheses that pooled it.
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Who cites it
44 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Meteorological factors cannot be ignored in machine learning-based methods for predicting dengue, a systematic review.International journal of biometeorology · 2024Pooled it
- A systematic review of dengue outbreak prediction models: Current scenario and future directions.PLoS neglected tropical diseases · 2023Pooled it
- Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review.Frontiers in public health · 2022Pooled it
- Global Genomic Surveillance.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Public health informatics tools for dengue risk management: A systematic review.PLOS digital health · 2026Article
- Anti-Dengue IgG Seroprevalence and Exposure-Related Risk in Italian Military Personnel Deployed on Overseas Missions: A Cross-Sectional Study.Tropical medicine and infectious disease · 2026Article
- Spatiotemporal dynamics of dengue: a town level study in Guangdong, China.BMC infectious diseases · 2026Article
- Dengue fever prediction based on meteorological features and deep learning models.Infectious Disease Modelling · 2026Article
- Advances in mosquito-borne disease surveillance using machine learning.New microbes and new infections · 2026Article
- Bridging ensemble model and public health practice: an approach for refining understanding of seasonal dengue transmission patterns in Bangladesh.BMC infectious diseases · 2026Article
- DengueGNN: Graph-based deep learning for modeling disease spread dynamics and prediction.Scientific reports · 2026Article
- Dengue forecasting and outbreak detection in Brazil using LSTM: integrating human mobility and climate factors.Infectious Disease Modelling · 2026Article
- Impact of projected climate and socioeconomic scenarios on state-wise annual dengue incidence in India using ensemble models.PLoS neglected tropical diseases · 2026Article
- Forecasting and Early Warning Systems for Dengue Outbreaks: Updated Narrative Review.Revista da Sociedade Brasileira de Medicina Tropical · 2026Review
- Machine learning reveals drivers of cold-related illness during energy infrastructure attacks in Wartime Ukraine.Scientific reports · 2025Article
- The Effect of Climate Change on Emergence and Evolution of Zoonotic Diseases in Asia.Zoonoses and public health · 2025Review
- Investigating linkages between human movement and meteorological variables on dengue outbreaks in the Pacific Islands.PLoS neglected tropical diseases · 2025Article
- Forecasting Dengue: Evaluating the Role of Hydroclimate Information in Subseasonal to Seasonal Prediction.GeoHealth · 2025Article
- Article
- Ensemble approaches for short-term dengue fever forecasts: A global evaluation study.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
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10 authors.
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Abstract
The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito-borne disease forecasting. In this study, two sets of RF models were developed at the national (pooled department-level data) and department level in Colombia to predict weekly dengue cases for 12-weeks ahead. A pooled national model based on artificial neural networks (ANN) was also developed and used as a comparator to the RF models. The various predictors included historic dengue cases, satellite-derived estimates for vegetation, precipitation, and air temperature, as well as population counts, income inequality, and education. Our RF model trained on the pooled national data was more accurate for department-specific weekly dengue cases estimation compared to a local model trained only on the department's data. Additionally, the forecast errors of the national RF model were smaller to those of the national pooled ANN model and were increased with the forecast horizon increasing from one-week-ahead (mean absolute error, MAE: 9.32) to 12-weeks ahead (MAE: 24.56). There was considerable variation in the relative importance of predictors dependent on forecast horizon. The environmental and meteorological predictors were relatively important for short-term dengue forecast horizons while socio-demographic predictors were relevant for longer-term forecast horizons. This study demonstrates the potential of RF in dengue forecasting with a feasible approach of using a national pooled model to forecast at finer spatial scales. Furthermore, including sociodemographic predictors is likely to be helpful in capturing longer-term dengue trends.
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