|Title:||Challenges and implications of predicting the spatiotemporal distribution of dengue fever outbreak in Chinese Taiwan using remote sensing data and deep learning||Authors:||Anno, Sumiko
|Keywords:||Deep learning;U-Net;dengue fever;spatiotemporal distribution||Issue Date:||14-Jan-2023||Publisher:||TAYLOR & FRANCIS LTD||Source:||GEO-SPATIAL INFORMATION SCIENCE||Abstract:||
Ongoing 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 predicting the spatiotemporal distribution of dengue fever outbreaks, we can effectively implement dengue fever prevention and control. Our study aims to predict the spatiotemporal distribution of dengue fever outbreaks in Chinese Taiwan using a U-Net based encoder - decoder model with daily datasets of sea-surface temperature, rainfall, and shortwave radiation from Remote Sensing (RS) instruments and dengue fever case notification data. Although the prediction accuracy of the proposed model was low and the overlapping areas between the ground truth and pixelwise prediction were few, some of the pixels were located nearby the ground truth, suggesting that the application of RS data and deep learning may help to predict the spatiotemporal distribution of dengue fever outbreaks. With further improvements, the deep learning model might effectively learn a small amount of training data for a specific task.
|Appears in Collections:||環境生物與漁業科學學系|
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