ArticlePLoS neglected tropical diseases2025
Dengue nowcasting in Brazil by combining official surveillance data and Google Trends information.
Article in PLoS neglected tropical diseases, 2025. 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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Who cites it
4 citing papers in PubMed.
- Predicting dengue where surveillance is weak: A case for climate- and search-based machine learning in Somalia.New microbes and new infections · 2026Article
- Dengue forecasting and outbreak detection in Brazil using LSTM: integrating human mobility and climate factors.Infectious Disease Modelling · 2026Article
- Epidemiological and digital syndromic surveillance data on dengue, chikungunya, and SARI in Brazil.Scientific data · 2026Article
- Forecasting and Early Warning Systems for Dengue Outbreaks: Updated Narrative Review.Revista da Sociedade Brasileira de Medicina Tropical · 2026Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Dengue is a mosquito-borne viral disease that poses significant public health challenges in tropical and sub-tropical regions worldwide. Surveillance systems are essential for dengue prevention and control. However, traditional systems often rely on delayed data, limiting their effectiveness. To address this, nowcasting methods are needed to estimate underreported cases, enabling more timely decision-making. This study evaluates the value of using Google Trends indices of dengue-related keywords to complement official dengue data for nowcasting dengue in Brazil, a country frequently affected by this disease. We compare various nowcasting approaches that incorporate autoregressive features from official dengue cases, Google Trends data, and a combination of both, using a naive approach as a baseline. The performance of these methods is evaluated by nowcasting weekly dengue cases from March 2024 to January 2025 across Brazilian states. Error measures and 50% and 95% coverage probabilities reveal that models incorporating Google Trends data enhance the accuracy of weekly nowcasts across states and offer valuable insights into dengue activity levels. To support real-time decision-making, we also present Dengue Tracker, a website that displays weekly dengue nowcasts and trends to inform both decision-makers and the public, improving situational awareness of dengue activity. In conclusion, the study demonstrates the value of digital data sources in enhancing dengue nowcasting, and emphasizes the value of integrating alternative data streams into traditional surveillance systems for better-informed decision-making.
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Registered trials
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