Prediction for Sensor-Less Locations Using Multi-View Graph Fusion Approach with Approximation Module: A Case Study on Dengue Fever Risk Sensor

Dengue fever is an emergency disease spread by mosquitoes. The most direct way to prevent the disease is to predict risky areas and bolster mosquito preventive strategies. Risk is usually evaluated by monitoring the number of eggs in the ovitraps set up by the government. However, areas without sensors still need to be checked and 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 paucity of historical data makes this endeavor challenging. Furthermore, determining how to effectively blend different features is another important research challenge and practical issue. We propose a Multi-View Graph Fusion Approach with Approximation Module (MVGAM) to address these two issues. For the regions that have no sensor coverage, MVGAM first uses a feature extractor to learn their representation based on their dynamic and static features. Then, we use a graph constructor to formulate the relationship between sensors from different perspectives and a multi-view graph fusion module to learn the embedding of sensors. Finally, we use an approximation module to deal with the lack of historical data. We conducted experiments using a real-world dataset from the urban area of Tainan, Taiwan. The results show that the proposed MVGAM outperforms the state-of-the-art methods and baselines. The ablation study also shows that every component in MVGAM has a significant impact on boosting the prediction effectiveness.