Global forecasting models for dengue outbreaks in endemic regions: a systematic review
Background. Dengue is a rapidly spreading mosquito-borne disease, posing significant global health challenges, particularly in endemic regions. Recent years have witnessed an increase in the frequency and intensity of dengue outbreaks, necessitating robust forecasting models for early intervention....
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| Format: | Article |
| Language: | Russian |
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Central Research Institute for Epidemiology
2025-07-01
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| Series: | Журнал микробиологии, эпидемиологии и иммунобиологии |
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| Online Access: | https://microbiol.crie.ru/jour/article/viewFile/18837/1611 |
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| author | Agung Sutriyawan Mursid Rahardjo Martini Martini Dwi Sutiningsih Cheerawit Rattanapan Nur Faeza Abu Kassim |
| author_facet | Agung Sutriyawan Mursid Rahardjo Martini Martini Dwi Sutiningsih Cheerawit Rattanapan Nur Faeza Abu Kassim |
| author_sort | Agung Sutriyawan |
| collection | DOAJ |
| description | Background. Dengue is a rapidly spreading mosquito-borne disease, posing significant global health challenges, particularly in endemic regions. Recent years have witnessed an increase in the frequency and intensity of dengue outbreaks, necessitating robust forecasting models for early intervention.
This systematic review aims to synthesize recent literature on dengue forecasting models, evaluate their predictive performance, and identify the most effective approaches.
Materials and methods. A comprehensive search in Scopus, PubMed, ScienceDirect, and Springer databases was conducted following PRISMA guidelines. Studies were selected based on strict inclusion and exclusion criteria, and the quality of the research was evaluated using TRIPOD criteria. Out of 1,366 identified studies, 13 met the eligibility criteria. Data were extracted and analyzed to assess the accuracy and validity of the forecasting models employed.
Results. The findings indicate that machine learning-based models, particularly random forest, outperform conventional statistical models such as ARIMA and Poisson regression. Additionally, climate data — especially temperature and rainfall play a critical role in forecasting dengue incidence.
Conclusion. The present study corroborates the superior efficacy of machine learning-based forecasting models, particularly random forest, in forecasting dengue cases compared to conventional statistical methods. This finding provides a foundation for the development of an enhanced early warning system to address future outbreaks of dengue. |
| format | Article |
| id | doaj-art-cbf8904fcc6649aa912c61dc577dc2a4 |
| institution | DOAJ |
| issn | 0372-9311 2686-7613 |
| language | Russian |
| publishDate | 2025-07-01 |
| publisher | Central Research Institute for Epidemiology |
| record_format | Article |
| series | Журнал микробиологии, эпидемиологии и иммунобиологии |
| spelling | doaj-art-cbf8904fcc6649aa912c61dc577dc2a42025-08-20T03:09:44ZrusCentral Research Institute for EpidemiologyЖурнал микробиологии, эпидемиологии и иммунобиологии0372-93112686-76132025-07-01102333134210.36233/0372-9311-6942830Global forecasting models for dengue outbreaks in endemic regions: a systematic reviewAgung Sutriyawan0https://orcid.org/0000-0002-6119-6073Mursid Rahardjo1https://orcid.org/0000-0003-4791-1242Martini Martini2https://orcid.org/0000-0002-6773-1727Dwi Sutiningsih3https://orcid.org/0000-0002-4128-6688Cheerawit Rattanapan4https://orcid.org/0000-0002-1799-422XNur Faeza Abu Kassim5https://orcid.org/0000-0001-6620-8603Diponegoro UniversityDiponegoro UniversityDiponegoro UniversityDiponegoro UniversityMahidol UniversityUniversiti Sains MalaysiaBackground. Dengue is a rapidly spreading mosquito-borne disease, posing significant global health challenges, particularly in endemic regions. Recent years have witnessed an increase in the frequency and intensity of dengue outbreaks, necessitating robust forecasting models for early intervention. This systematic review aims to synthesize recent literature on dengue forecasting models, evaluate their predictive performance, and identify the most effective approaches. Materials and methods. A comprehensive search in Scopus, PubMed, ScienceDirect, and Springer databases was conducted following PRISMA guidelines. Studies were selected based on strict inclusion and exclusion criteria, and the quality of the research was evaluated using TRIPOD criteria. Out of 1,366 identified studies, 13 met the eligibility criteria. Data were extracted and analyzed to assess the accuracy and validity of the forecasting models employed. Results. The findings indicate that machine learning-based models, particularly random forest, outperform conventional statistical models such as ARIMA and Poisson regression. Additionally, climate data — especially temperature and rainfall play a critical role in forecasting dengue incidence. Conclusion. The present study corroborates the superior efficacy of machine learning-based forecasting models, particularly random forest, in forecasting dengue cases compared to conventional statistical methods. This finding provides a foundation for the development of an enhanced early warning system to address future outbreaks of dengue.https://microbiol.crie.ru/jour/article/viewFile/18837/1611dengueforecast modelmachine learningrandom forestearly warning system |
| spellingShingle | Agung Sutriyawan Mursid Rahardjo Martini Martini Dwi Sutiningsih Cheerawit Rattanapan Nur Faeza Abu Kassim Global forecasting models for dengue outbreaks in endemic regions: a systematic review Журнал микробиологии, эпидемиологии и иммунобиологии dengue forecast model machine learning random forest early warning system |
| title | Global forecasting models for dengue outbreaks in endemic regions: a systematic review |
| title_full | Global forecasting models for dengue outbreaks in endemic regions: a systematic review |
| title_fullStr | Global forecasting models for dengue outbreaks in endemic regions: a systematic review |
| title_full_unstemmed | Global forecasting models for dengue outbreaks in endemic regions: a systematic review |
| title_short | Global forecasting models for dengue outbreaks in endemic regions: a systematic review |
| title_sort | global forecasting models for dengue outbreaks in endemic regions a systematic review |
| topic | dengue forecast model machine learning random forest early warning system |
| url | https://microbiol.crie.ru/jour/article/viewFile/18837/1611 |
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