A District-level Ensemble Model to Enhance Dengue Prediction and Control for the Mekong Delta Region of Vietnam
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866915073153302528 |
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| author | Areed, Wala Draidi Nguyen, Thi Thanh Thao Do, Kien Quoc Nguyen, Thinh Bui, Vinh Nelson, Elisabeth Warren, Joshua L. Doan, Quang-Van Sinh, Nam Vu Osborne, Nicholas Richards, Russell Tran, Nu Quy Linh Le, Hong Pham, Tuan Hung, Trinh Manh Nghiem, Son Phung, Hai Chu, Cordia Dubrow, Robert Weinberger, Daniel M. Phung, Dung |
| author_facet | Areed, Wala Draidi Nguyen, Thi Thanh Thao Do, Kien Quoc Nguyen, Thinh Bui, Vinh Nelson, Elisabeth Warren, Joshua L. Doan, Quang-Van Sinh, Nam Vu Osborne, Nicholas Richards, Russell Tran, Nu Quy Linh Le, Hong Pham, Tuan Hung, Trinh Manh Nghiem, Son Phung, Hai Chu, Cordia Dubrow, Robert Weinberger, Daniel M. Phung, Dung |
| contents | The Mekong Delta Region of Vietnam faces increasing dengue risks driven by urbanization, globalization, and climate change. This study introduces a probabilistic forecasting model for predicting dengue incidence and outbreaks with one to three month lead times, integrating meteorological, sociodemographic, preventive, and epidemiological data. Seventy-two models were evaluated, and an ensemble combining top-performing spatiotemporal, supervised PCA, and semi-mechanistic hhh4 frameworks was developed. Using data from 2004-2022 for training, validation, and evaluation, the ensemble model demonstrated 69% accuracy at a 3-month horizon, outperforming a baseline model. While effective, its performance declined in years with atypical seasonality, such as 2019 and 2022. The model provides critical lead time for targeted dengue prevention and control measures, addressing a growing public health need in the region. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_15645 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A District-level Ensemble Model to Enhance Dengue Prediction and Control for the Mekong Delta Region of Vietnam Areed, Wala Draidi Nguyen, Thi Thanh Thao Do, Kien Quoc Nguyen, Thinh Bui, Vinh Nelson, Elisabeth Warren, Joshua L. Doan, Quang-Van Sinh, Nam Vu Osborne, Nicholas Richards, Russell Tran, Nu Quy Linh Le, Hong Pham, Tuan Hung, Trinh Manh Nghiem, Son Phung, Hai Chu, Cordia Dubrow, Robert Weinberger, Daniel M. Phung, Dung Applications The Mekong Delta Region of Vietnam faces increasing dengue risks driven by urbanization, globalization, and climate change. This study introduces a probabilistic forecasting model for predicting dengue incidence and outbreaks with one to three month lead times, integrating meteorological, sociodemographic, preventive, and epidemiological data. Seventy-two models were evaluated, and an ensemble combining top-performing spatiotemporal, supervised PCA, and semi-mechanistic hhh4 frameworks was developed. Using data from 2004-2022 for training, validation, and evaluation, the ensemble model demonstrated 69% accuracy at a 3-month horizon, outperforming a baseline model. While effective, its performance declined in years with atypical seasonality, such as 2019 and 2022. The model provides critical lead time for targeted dengue prevention and control measures, addressing a growing public health need in the region. |
| title | A District-level Ensemble Model to Enhance Dengue Prediction and Control for the Mekong Delta Region of Vietnam |
| topic | Applications |
| url | https://arxiv.org/abs/2412.15645 |