ArticleProceedings of the National Academy of Sciences of the United States of America2026
Leveraging probabilistic forecasts for dengue preparedness and control: The 2024 Dengue Forecasting Sprint in Brazil.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Quantifying, Understanding, and Correcting for Delays in Routine Dengue Case Reporting in the Americas.The American journal of tropical medicine and hygiene · 2026Article
- Rethinking Dengue Preparedness in the Era of Climate Change, Urbanisation, and Digital Health: A Structured Narrative Review.Medicina (Kaunas, Lithuania) · 2026Review
- Modeling treatment and temperature effects on dengue transmission at the division level in Bangladesh.PloS one · 2026Article
- Dengue nowcasting in Brazil by combining official surveillance data and Google Trends information.PLoS neglected tropical diseases · 2025Article
Corrections and comments
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Authors and funding
25 authors.
Funding
Abstract
Forecast models are a key decision-support tool for public health authorities in managing epidemics, feeding into early warning systems, scenario evaluations, and an empirical basis for resource allocation. In Brazil, improving dengue forecasting became a priority in response to the unprecedented increase in cases, which surpassed the total of the previous decade and expanded to new regions. The Infodengue-Mosqlimate consortium launched the Infodengue-Mosqlimate Dengue Challenge 2024 (IMDC24), or Dengue Forecast Sprint, bringing together six international teams provided with cases and climate covariates data to generate actionable forecasts for 2024 and 2025 seasons in five diverse Brazilian states, leveraging advanced machine learning and classical statistical models. This paper outlines the structure and findings of the IMDC24. The performance of the models varied between years and locations, and no single model consistently excelled, especially during 2024's unprecedentedly large season. This performance variability highlighted the need for ensemble approaches. The ensemble models developed are presented as the main results of this collaborative development. As intended, the ensemble models have been adopted by Brazilian public health authorities to help with planning and response to the forecasted 2025 dengue epidemics across the country.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.