دورية أكاديمية
A reproducible ensemble machine learning approach to forecast dengue outbreaks.
| العنوان: | A reproducible ensemble machine learning approach to forecast dengue outbreaks. |
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| المؤلفون: | Sebastianelli A; Engineering Department, University of Sannio, Benevento, Italy. alessandro.sebastianelli@esa.int.; European Space Agency, Φ-lab, Frascati, Italy. alessandro.sebastianelli@esa.int., Spiller D; School of Aerospace Engineering, Sapienza University of Rome, Rome, Italy., Carmo R; European Space Agency, Φ-lab, Frascati, Italy., Wheeler J; European Space Agency, Φ-lab, Frascati, Italy., Nowakowski A; Faculty of Geodesy and Cartography, Warsaw University of Technology, Warsaw, Poland., Jacobson LV; Statistics Department, Fluminense Federal University, Niterói, Brazil., Kim D; UNICEF, New York, NY, USA., Barlevi H; UNICEF, New York, NY, USA., Cordero ZER; UNICEF, New York, NY, USA., Colón-González FJ; Wellcome Trust, Data for Science and Health, London, UK.; Centre on Climate Change and Planetary Health and Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.; Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich, UK., Lowe R; Centre on Climate Change and Planetary Health and Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.; Barcelona Supercomputing Center (BSC), Barcelona, Spain.; Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain., Ullo SL; Engineering Department, University of Sannio, Benevento, Italy., Schneider R; European Space Agency, Φ-lab, Frascati, Italy. rochelle.schneider@esa.int. |
| المصدر: | Scientific reports [Sci Rep] 2024 Feb 15; Vol. 14 (1), pp. 3807. Date of Electronic Publication: 2024 Feb 15. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: London : Nature Publishing Group, copyright 2011- |
| مواضيع طبية MeSH: | Dengue*/epidemiology, Humans ; Young Adult ; Adult ; Disease Outbreaks/prevention & control ; Public Health/methods ; Climate ; Machine Learning |
| مستخلص: | Dengue fever, a prevalent and rapidly spreading arboviral disease, poses substantial public health and economic challenges in tropical and sub-tropical regions worldwide. Predicting infectious disease outbreaks on a countrywide scale is complex due to spatiotemporal variations in dengue incidence across administrative areas. To address this, we propose a machine learning ensemble model for forecasting the dengue incidence rate (DIR) in Brazil, with a focus on the population under 19 years old. The model integrates spatial and temporal information, providing one-month-ahead DIR estimates at the state level. Comparative analyses with a dummy model and ablation studies demonstrate the ensemble model's qualitative and quantitative efficacy across the 27 Brazilian Federal Units. Furthermore, we showcase the transferability of this approach to Peru, another Latin American country with differing epidemiological characteristics. This timely forecast system can aid local governments in implementing targeted control measures. The study advances climate services for health by identifying factors triggering dengue outbreaks in Brazil and Peru, emphasizing collaborative efforts with intergovernmental organizations and public health institutions. The innovation lies not only in the algorithms themselves but in their application to a domain marked by data scarcity and operational scalability challenges. We bridge the gap by integrating well-curated ground data with advanced analytical methods, addressing a significant deficiency in current practices. The successful transfer of the model to Peru and its consistent performance during the 2019 outbreak in Brazil showcase its scalability and practical application. While acknowledging limitations in handling extreme values, especially in regions with low DIR, our approach excels where accurate predictions are critical. The study not only contributes to advancing DIR forecasting but also represents a paradigm shift in integrating advanced analytics into public health operational frameworks. This work, driven by a collaborative spirit involving intergovernmental organizations and public health institutions, sets a precedent for interdisciplinary collaboration in addressing global health challenges. It not only enhances our understanding of factors triggering dengue outbreaks but also serves as a template for the effective implementation of advanced analytical methods in public health. (© 2024. The Author(s).) |
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| معلومات مُعتمدة: | United Kingdom WT_ Wellcome Trust |
| تواريخ الأحداث: | Date Created: 20240215 Date Completed: 20240219 Latest Revision: 20240220 |
| رمز التحديث: | 20240220 |
| مُعرف محوري في PubMed: | PMC10869339 |
| DOI: | 10.1038/s41598-024-52796-9 |
| PMID: | 38360915 |
| قاعدة البيانات: | MEDLINE |
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Full Text Finder | تدمد: | 2045-2322 |
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| DOI: | 10.1038/s41598-024-52796-9 |