ArticleJournal of the Royal Society, Interface2021
A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles.
Article in Journal of the Royal Society, Interface, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 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
- How climate change affects mosquito-borne infectious disease: The different trajectories of malaria and dengue.Science advances · 2026Review
- Advances in mosquito-borne disease surveillance using machine learning.New microbes and new infections · 2026Article
- Generative artificial intelligence in public health: a framework for governance and systemic integration.Frontiers in medicine · 2026Review
- Forecasting and Early Warning Systems for Dengue Outbreaks: Updated Narrative Review.Revista da Sociedade Brasileira de Medicina Tropical · 2026Review
- 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
- Annual global dengue dynamics are related to multi-source factors revealed by a machine learning prediction analysis.PLoS neglected tropical diseases · 2025Article
- Leveraging Climate Data for Dengue Forecasting in Ba Ria Vung Tau Province, Vietnam: An Advanced Machine Learning Approach.Tropical medicine and infectious disease · 2024Article
- Hybrid Machine Learning Approach to Zero-Inflated Data Improves Accuracy of Dengue Prediction.PLoS neglected tropical diseases · 2024Article
- A reproducible ensemble machine learning approach to forecast dengue outbreaks.Scientific reports · 2024Article
- Multimodal approach to public health interventions using EGG and mobile health technologies.Frontiers in public health · 2024Article
- Impact of climate change on SARS-CoV-2 epidemic in China.PloS one · 2023Article
- Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil.International journal of environmental research and public health · 2022Article
- How heterogeneous is the dengue transmission profile in Brazil? A study in six Brazilian states.PLoS neglected tropical diseases · 2022Article
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- Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil.PLoS neglected tropical diseases · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
Transmission of dengue fever depends on a complex interplay of human, climate and mosquito dynamics, which often change in time and space. It is well known that its disease dynamics are highly influenced by multiple factors including population susceptibility to infection as well as by microclimates: small-area climatic conditions which create environments favourable for the breeding and survival of mosquitoes. Here, we present a novel machine learning dengue forecasting approach, which, dynamically in time and space, identifies local patterns in weather and population susceptibility to make epidemic predictions at the city level in Brazil, months ahead of the occurrence of disease outbreaks. Weather-based predictions are improved when information on population susceptibility is incorporated, indicating that immunity is an important predictor neglected by most dengue forecast models. Given the generalizability of our methodology to any location or input data, it may prove valuable for public health decision-making aimed at mitigating the effects of seasonal dengue outbreaks in locations globally.
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Registered trials
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