A reproducible ensemble machine learning approach to forecast dengue outbreaks
Abstract 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 inc...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-52796-9 |
| _version_ | 1827327831062872064 |
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| author | Alessandro Sebastianelli Dario Spiller Raquel Carmo James Wheeler Artur Nowakowski Ludmilla Viana Jacobson Dohyung Kim Hanoch Barlevi Zoraya El Raiss Cordero Felipe J Colón-González Rachel Lowe Silvia Liberata Ullo Rochelle Schneider |
| author_facet | Alessandro Sebastianelli Dario Spiller Raquel Carmo James Wheeler Artur Nowakowski Ludmilla Viana Jacobson Dohyung Kim Hanoch Barlevi Zoraya El Raiss Cordero Felipe J Colón-González Rachel Lowe Silvia Liberata Ullo Rochelle Schneider |
| author_sort | Alessandro Sebastianelli |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-07T15:08:07Z |
| format | Article |
| id | doaj.art-54fe9127b4a14bdc9064b38605d347cc |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:08:07Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-54fe9127b4a14bdc9064b38605d347cc2024-03-05T18:47:35ZengNature PortfolioScientific Reports2045-23222024-02-0114111710.1038/s41598-024-52796-9A reproducible ensemble machine learning approach to forecast dengue outbreaksAlessandro Sebastianelli0Dario Spiller1Raquel Carmo2James Wheeler3Artur Nowakowski4Ludmilla Viana Jacobson5Dohyung Kim6Hanoch Barlevi7Zoraya El Raiss Cordero8Felipe J Colón-González9Rachel Lowe10Silvia Liberata Ullo11Rochelle Schneider12Engineering Department, University of SannioSchool of Aerospace Engineering, Sapienza University of RomeEuropean Space Agency, Φ-labEuropean Space Agency, Φ-labFaculty of Geodesy and Cartography, Warsaw University of TechnologyStatistics Department, Fluminense Federal UniversityUNICEFUNICEFUNICEFWellcome Trust, Data for Science and HealthCentre on Climate Change and Planetary Health and Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical MedicineEngineering Department, University of SannioEuropean Space Agency, Φ-labAbstract 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.https://doi.org/10.1038/s41598-024-52796-9 |
| spellingShingle | Alessandro Sebastianelli Dario Spiller Raquel Carmo James Wheeler Artur Nowakowski Ludmilla Viana Jacobson Dohyung Kim Hanoch Barlevi Zoraya El Raiss Cordero Felipe J Colón-González Rachel Lowe Silvia Liberata Ullo Rochelle Schneider A reproducible ensemble machine learning approach to forecast dengue outbreaks Scientific Reports |
| title | A reproducible ensemble machine learning approach to forecast dengue outbreaks |
| title_full | A reproducible ensemble machine learning approach to forecast dengue outbreaks |
| title_fullStr | A reproducible ensemble machine learning approach to forecast dengue outbreaks |
| title_full_unstemmed | A reproducible ensemble machine learning approach to forecast dengue outbreaks |
| title_short | A reproducible ensemble machine learning approach to forecast dengue outbreaks |
| title_sort | reproducible ensemble machine learning approach to forecast dengue outbreaks |
| url | https://doi.org/10.1038/s41598-024-52796-9 |
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