ArticleScientific reports2022
Data-driven computational intelligence applied to dengue outbreak forecasting: a case study at the scale of the city of Natal, RN-Brazil.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Forecasting and Early Warning Systems for Dengue Outbreaks: Updated Narrative Review.Revista da Sociedade Brasileira de Medicina Tropical · 2026Review
- [Presence of Aedes aegypti in a high altitude area in Bolivia]Revista de la Facultad de Ciencias Medicas (Cordoba, Argentina) · 2025Article
- An explainable covariate compartmental model for predicting the spatio-temporal patterns of dengue in Sri Lanka.PLoS computational biology · 2025Article
- Systematic Review of the Ovitrap Surveillance ofTropical medicine and infectious disease · 2025Review
- ARBOALVO: A Bayesian spatiotemporal learning and predictive model for dengue cases in the endemic Northeast city of Natal, Rio Grande do Norte, Brazil.PLoS neglected tropical diseases · 2025Article
- Influence of climate and heatwaves on dengue transmission in Sao Paulo and Natal, Brazil.PloS one · 2025Article
- Development of an Integrated Surveillance System to Improve Preparedness for Arbovirus Outbreaks in a Dengue Endemic Setting: Descriptive Study.JMIR public health and surveillance · 2024Article
- Impact of storm drains on the maintenance of dengue endemicity in Presidente Prudente, São Paulo, Brazil: a geospatial and epidemiologic approach.Frontiers in public health · 2024Article
- Integrating gated recurrent unit in graph neural network to improve infectious disease prediction: an attempt.Frontiers in public health · 2024Article
- Machine learning and molecular docking prediction of potential inhibitors against dengue virus.Frontiers in chemistry · 2024Article
- Dengue Prediction in Latin America Using Machine Learning and the One Health Perspective: A Literature Review.Tropical medicine and infectious disease · 2022Review
- The practicality of Malaysia dengue outbreak forecasting model as an early warning system.Infectious Disease Modelling · 2022Article
- Temporal Variations and Spatial Clusters of Dengue in Thailand: Longitudinal Study before and during the Coronavirus Disease (COVID-19) Pandemic.Tropical medicine and infectious disease · 2022Article
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9 authors.
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Abstract
Dengue is recognized as a health problem that causes significant socioeconomic impacts throughout the world, affecting millions of people each year. A commonly used method for monitoring the dengue vector is to count the eggs that Aedes aegypti mosquitoes have laid in spatially distributed ovitraps. Given this approach, the present study uses a database collected from 397 ovitraps allocated across the city of Natal, RN-Brazil. The Egg Density Index for each neighborhood was computed weekly, over four complete years (from 2016 to 2019), and simultaneously analyzed with the dengue case incidence. Our results illustrate that the incidence of dengue is related to the socioeconomic level of the neighborhoods in the city of Natal. A deep learning algorithm was used to predict future dengue case incidence, either based on the previous weeks of dengue incidence or the number of eggs present in the ovitraps. The analysis reveals that ovitrap data allows earlier prediction (four to six weeks) compared to dengue incidence itself (one week). Therefore, the results validate that the quantification of Aedes aegypti eggs can be valuable for the early planning of public health interventions.
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