Forecasting Dengue Fever Risk in Regions without Sensors Using Multi-View Graph Fusion Recurrent Neural Network

Pei Xuan Li, Hsun Ping Hsieh

研究成果: Conference contribution

摘要

Dengue fever is an emergency disease spread by mosquitoes. The most direct way to prevent the disease is to predict risky areas and increase mosquito preventive strategies. Risk is evaluated by monitoring the sensors set up by the government. However, areas without sensors still need to be managed for dengue risk. In this study, we focus on forecasting each region's fine-grained dengue fever risk, especially in regions without sensor coverage. The lack of historical data makes this endeavor challenging. Furthermore, determining how to effectively blend different features is another challenge. We propose a Multi-View Graph Fusion Recurrent Neural Network (MVGFRNN), which consists of a multi-view graph constructor, graph fusion module, and an approximation module to address these two issues. We conducted experiments using a real-world dataset from the urban area of Tainan, Taiwan. The results show that MVGFRNN outperforms state-of-the-art methods.

原文English
主出版物標題31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023
編輯Maria Luisa Damiani, Matthias Renz, Ahmed Eldawy, Peer Kroger, Mario A. Nascimento
發行者Association for Computing Machinery
ISBN(電子)9798400701689
DOIs
出版狀態Published - 2023 11月 13
事件31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023 - Hamburg, Germany
持續時間: 2023 11月 132023 11月 16

出版系列

名字GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Conference

Conference31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023
國家/地區Germany
城市Hamburg
期間23-11-1323-11-16

All Science Journal Classification (ASJC) codes

  • 地表過程
  • 電腦科學應用
  • 建模與模擬
  • 電腦繪圖與電腦輔助設計
  • 資訊系統

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