Challenges and implications of predicting the spatiotemporal distribution of dengue fever outbreak in Chinese Taiwan using remote sensing data and deep learning

Challenges and implications of predicting the spatiotemporal distribution of dengue fever outbreak in Chinese Taiwan using remote sensing data and deep learning

ABSTRACTOngoing climate change has accelerated the outbreak and expansion of climate-sensitive infectious diseases such as dengue fever. Dengue fever will remain a threat until safe and effective vaccines and antiviral drugs have been developed, distributed, and administered on a global scale. By pr...

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Bibliographic Details
Main Authors: Sumiko Anno, Hirakawa Tsubasa, Satoru Sugita, Shinya Yasumoto, Ming-An Lee, Yoshinobu Sasaki, Kei Oyoshi
Format: Article
Language:English
Published: Taylor & Francis Group 2023-01-01
Series:Geo-spatial Information Science
Subjects:
Deep learning
U-Net
dengue fever
spatiotemporal distribution
Online Access:https://www.tandfonline.com/doi/10.1080/10095020.2022.2144770

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https://www.tandfonline.com/doi/10.1080/10095020.2022.2144770

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