| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2026-02-10 |
| タイトル |
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|
タイトル |
Application of a Temporal Fusion Transformer and Long-Term Climate and Disease Data to Assess the Predictive Power and Understand the Drivers for Malaria and Dengue |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
deep learning |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
climate–disease interactions |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
vector-borne disease forecasting |
| 資源タイプ |
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|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| 著者 |
Pillay, Micheal Teron
Le, Mai Thi Quỳnh
Takamatsu, Yuki
Phong, Tran Vu
Kgalane, Nyakallo
Minakawa, Noboru
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| 抄録 |
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内容記述タイプ |
Abstract |
|
内容記述 |
Vector-borne diseases are strongly influenced by climate, yet the magnitude and temporal variability of climate–disease relationships remain poorly quantified. Outbreaks occur abruptly, and responses are typically delayed, underscoring the need for predictive tools that can support proactive interventions. This study applies Temporal Fusion Transformers (TFTs) to long-term, high-resolution climate datasets and to weekly malaria and dengue case records from South Africa and Vietnam to assess predictive performance and identify key environmental drivers. The models incorporated diverse climatic predictors and large-scale climate indices and were trained using multi-horizon forecasting with novel loss functions and physics-based constraints. The best malaria model achieved an R2 of 0.95 and an MAE of 4.98, while leading dengue models reached R2 values up to 0.90. Variable-importance analyses derived from model-learned weights showed that extreme temperature and rainfall metrics were consistently the strongest predictors, with ENSO (El Niño Southern Oscillation) and IOD (Indian Ocean Dipole) improving longer-range malaria forecasts. Furthermore, climate–disease risk dynamics were explored, revealing specific temperature and rainfall thresholds associated with elevated transmission and highlighting non-stationary relationships across decades. These findings demonstrate accurate, interpretable forecasting offered by TFTs and represent a valuable tool for early warning and understanding of complex climate–disease interactions. |
|
言語 |
en |
| 書誌情報 |
en : International Journal of Environmental Research and Public Health
巻 23,
号 1,
p. art. no. 75,
発行日 2026-01-05
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| 出版者 |
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出版者 |
MDPI |
|
言語 |
en |
| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
1660-4601 |
| DOI |
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|
関連タイプ |
isIdenticalTo |
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|
識別子タイプ |
DOI |
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|
関連識別子 |
https://doi.org/10.3390/ijerph23010075 |
| 権利 |
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|
権利情報 |
© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. |
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言語 |
en |
| 著者版フラグ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 引用 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
International journal of environmental research and public health, 23(1), art. no. 75; 2026 |
|
言語 |
en |
IJERPH23_75.pdf (14 MB)
