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

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.

Recommended citation: Pei-Xuan Li and Hsun-Ping Hsieh. 2023. Forecasting Dengue Fever Risk in Regions without Sensors Using Multi-View Graph Fusion Recurrent Neural Network. In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL '23). Association for Computing Machinery, New York, NY, USA, Article 86, 1–4. https://doi.org/10.1145/3589132.3625636
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