ArticleScientific data2025
Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Artificial intelligence for climate-health early warning systems in the Horn of Africa: opportunities, challenges, and a roadmap for action.Globalization and health · 2026Article
- Spatio-temporal forecasting of dengue in the Americas through hybrid mechanistic and data-driven models: Systematic review and meta-analysis.Infectious Disease Modelling · 2026Review
- Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing.Scientific data · 2025Article
- The Role of Big Data in Developing Innovative Predictive Learning Models for Neglected Tropical Diseases within the New Generation of the Evidence-Based Medicine Pyramid.Le infezioni in medicina · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dengue fever has been spreading rapidly worldwide, with a notably high prevalence in South American countries such as Brazil. Its transmission dynamics are governed by the vector population dynamics and the interactions among humans, vectors, and pathogens, which are further shaped by environmental factors. Calculating these environmental indicators is challenging due to the limited spatial coverage of weather station observations and the time-consuming processes involved in downloading and processing local data, such as satellite imagery. This issue is exacerbated in large-scale studies, making it difficult to develop comprehensive and publicly accessible datasets of disease-influencing factors. Addressing this challenge necessitates the efficient data integration methods and the assembly of multi-factorial datasets to aid public health authorities in understanding dengue transmission mechanisms and improving risk prediction models. In response, we developed a population-weighted dataset of 12 dengue risk factors, covering 558 microregions in Brazil over 1252 epidemiological weeks from 2001 to 2024. This dataset and the associated methodology streamline data processing for researchers and can be adapted for other vector-borne disease studies.
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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.