ArticlePloS one2019
Forecasting dengue fever in Brazil: An assessment of climate conditions.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 3 of them syntheses that pooled it.
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Who cites it
30 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
- Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review.Frontiers in public health · 2022Pooled it
- Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review.PLoS neglected tropical diseases · 2022Pooled it
- Advances in mosquito-borne disease surveillance using machine learning.New microbes and new infections · 2026Article
- Innovative application of a traffic-prediction spatio-temporal graph convolutional network for dengue disease forecasting.Scientific reports · 2026Article
- How technologies are driving digital innovation in mosquito surveillance systems: a global scoping review over the past decade.BMJ public health · 2026Article
- Forecasting and Early Warning Systems for Dengue Outbreaks: Updated Narrative Review.Revista da Sociedade Brasileira de Medicina Tropical · 2026Review
- A statistical model for forecasting probabilistic epidemic bands for dengue cases in Brazil.Infectious Disease Modelling · 2025Article
- Dengue Fever Resurgence in Iran: An Integrative Review of Causative Factors and Control Strategies.Tropical medicine and infectious disease · 2025Review
- 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
- Mapping the Incidence of Dengue Fever in the State of Pará, Eastern Amazon: Epidemiology and Relationships with Climate.Reports (MDPI) · 2025Article
- Integrating meteorological data and hybrid intelligent models for dengue fever prediction.BMC public health · 2025Article
- Geospatial clustering reveals dengue hotspots across Brazilian municipalities, 2024.Frontiers in public health · 2025Article
- Hybrid Machine Learning Approach to Zero-Inflated Data Improves Accuracy of Dengue Prediction.PLoS neglected tropical diseases · 2024Article
- Effects of the El Niño-Southern Oscillation and seasonal weather conditions on Aedes aegypti infestation in the State of São Paulo (Brazil): A Bayesian spatio-temporal study.PLoS neglected tropical diseases · 2024Article
- Spatiotemporal Characterization of Dengue Incidence and Its Correlation to Climate Parameters in Indonesia.Insects · 2024Article
- Improving dengue fever predictions in Taiwan based on feature selection and random forests.BMC infectious diseases · 2024Article
- A reproducible ensemble machine learning approach to forecast dengue outbreaks.Scientific reports · 2024Article
- Influence of temperature and precipitation on dengue incidence in Campinas, São Paulo State, Brazil (2013-2022).Revista da Sociedade Brasileira de Medicina Tropical · 2024Article
- Exploring the dynamics of monkeypox transmission with data-driven methods and a deterministic model.Frontiers in epidemiology · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Local climate conditions play a major role in the biology of the Aedes aegypti mosquito, the main vector responsible for transmitting dengue, zika, chikungunya and yellow fever in urban centers. For this reason, a detailed assessment of periods in which changes in climate conditions affect the number of human cases may improve the timing of vector-control efforts. In this work, we develop new machine-learning algorithms to analyze climate time series and their connection to the occurrence of dengue epidemic years for seven Brazilian state capitals. Our method explores the impact of two key variables-frequency of precipitation and average temperature-during a wide range of time windows in the annual cycle. Our results indicate that each Brazilian state capital considered has its own climate signatures that correlate with the overall number of human dengue-cases. However, for most of the studied cities, the winter preceding an epidemic year shows a strong predictive power. Understanding such climate contributions to the vector's biology could lead to more accurate prediction models and early warning systems.
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