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

Pei Xuan Li, Hsun Ping Hsieh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023
EditorsMaria Luisa Damiani, Matthias Renz, Ahmed Eldawy, Peer Kroger, Mario A. Nascimento
PublisherAssociation for Computing Machinery
ISBN (Electronic)9798400701689
DOIs
Publication statusPublished - 2023 Nov 13
Event31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023 - Hamburg, Germany
Duration: 2023 Nov 132023 Nov 16

Publication series

NameGIS: 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
Country/TerritoryGermany
CityHamburg
Period23-11-1323-11-16

All Science Journal Classification (ASJC) codes

  • Earth-Surface Processes
  • Computer Science Applications
  • Modelling and Simulation
  • Computer Graphics and Computer-Aided Design
  • Information Systems

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