Leveraging probabilistic forecasts for dengue preparedness and control: The 2024 Dengue Forecasting Sprint in Brazil

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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Publication Details

Journal
Proceedings of the National Academy of Sciences
Published
2026-02-12
DOI
https://doi.org/10.1073/pnas.2508989123
Primary Topic
Mosquito-borne diseases and control
Type
article
Field-Weighted Citation Impact
0.00
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article

Leveraging probabilistic forecasts for dengue preparedness and control: The 2024 Dengue Forecasting Sprint in Brazil

Cláudia Torres Codeço, Laís Picinini Freitas, Raquel Martins Lana, Marcio M. Bastos et al.
Proceedings of the National Academy of Sciences
Mosquito-borne diseases and control
article

Leveraging probabilistic forecasts for dengue preparedness and control: The 2024 Dengue Forecasting Sprint in Brazil

Cláudia Torres Codeço, Laís Picinini Freitas, Raquel Martins Lana, Marcio M. Bastos, Giovenale Moirano, X. Chen, Haridas Kumar Das, Iasmim Ferreira de Almeida, Fernanda Valente, Daniela Sofie Lührsen, Lucas M. Stolerman, L Bianchi, Luã B. Vacaro, Raul Capellan, Ramila Alencar, Luiz Max Carvalho, Paula Moraga, Eduardo Correa Araujo, R. Lowe, Leonardo S. Bastos, Flávio C. Coelho, Fabiana Ganem, Oswaldo Cruz, Chloe Fletcher, Americo Cunha
article en

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.

Proceedings of the National Academy of SciencesVol. 123(7)
Institució Catalana de Recerca i Estudis Avançats (ES), Instituto Nacional de Matemática Pura e Aplicada (BR), Brazilian Agricultural Research Corporation (BR), Barcelona Supercomputing Center (ES), Universidade do Estado do Rio de Janeiro (BR), Escola Nacional de Saúde Pública (BR), Oklahoma State University Oklahoma City (US), Laboratório Nacional de Computação Científica (BR), Norwegian Institute of Bioeconomy Research (NO), Fundação Getulio Vargas (BR), Toronto Metropolitan University (CA), King Abdullah University of Science and Technology (SA), Universidade Federal do Estado do Rio de Janeiro (BR), Universitat Politècnica de Catalunya (ES)
Climate action
Openalex Percentile: Top 60%
Mosquito-borne diseases and control
DOIOpen Access PDFOpenAlex
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