Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing
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 envir...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05045-1 |
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| author | Qixu Zhu Zhichao Li Jinwei Dong Ping Fu Qu Cheng Jun Cai Helen Gurgel Linsheng Yang |
| author_facet | Qixu Zhu Zhichao Li Jinwei Dong Ping Fu Qu Cheng Jun Cai Helen Gurgel Linsheng Yang |
| author_sort | Qixu Zhu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-68f2d1e94c2449c3b1ea150cfb9cda9d |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-68f2d1e94c2449c3b1ea150cfb9cda9d2025-08-20T03:52:24ZengNature PortfolioScientific Data2052-44632025-04-0112111110.1038/s41597-025-05045-1Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computingQixu Zhu0Zhichao Li1Jinwei Dong2Ping Fu3Qu Cheng4Jun Cai5Helen Gurgel6Linsheng Yang7Key Laboratory for Resource Use and Environmental Remediation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesKey Laboratory for Resource Use and Environmental Remediation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesKey Laboratory for Resource Use and Environmental Remediation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesSchool of Geographical Sciences, University of Nottingham Ningbo ChinaDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and TechnologySchool of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of EducationLaboratory of Geography, Environment and Health (LAGAS), Geography Department, Brasília University (UnB)Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesAbstract 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.https://doi.org/10.1038/s41597-025-05045-1 |
| spellingShingle | Qixu Zhu Zhichao Li Jinwei Dong Ping Fu Qu Cheng Jun Cai Helen Gurgel Linsheng Yang Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing Scientific Data |
| title | Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing |
| title_full | Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing |
| title_fullStr | Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing |
| title_full_unstemmed | Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing |
| title_short | Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing |
| title_sort | spatiotemporal dataset of dengue influencing factors in brazil based on geospatial big data cloud computing |
| url | https://doi.org/10.1038/s41597-025-05045-1 |
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