Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data
This work addresses the need for more accurate and timely pandemic forecasting for public health management, though it appears incremental as it combines existing techniques like graph neural networks and multi-modal data.
The paper tackles the problem of forecasting emerging pandemics by integrating temporal graph neural networks with multi-modal big data, such as social media, and demonstrates that their MGL4MEP framework outperforms baseline methods across various scenarios and prediction horizons.
Accurate forecasting and analysis of emerging pandemics play a crucial role in effective public health management and decision-making. Traditional approaches primarily rely on epidemiological data, overlooking other valuable sources of information that could act as sensors or indicators of pandemic patterns. In this paper, we propose a novel framework called MGL4MEP that integrates temporal graph neural networks and multi-modal data for learning and forecasting. We incorporate big data sources, including social media content, by utilizing specific pre-trained language models and discovering the underlying graph structure among users. This integration provides rich indicators of pandemic dynamics through learning with temporal graph neural networks. Extensive experiments demonstrate the effectiveness of our framework in pandemic forecasting and analysis, outperforming baseline methods across different areas, pandemic situations, and prediction horizons. The fusion of temporal graph learning and multi-modal data enables a comprehensive understanding of the pandemic landscape with less time lag, cheap cost, and more potential information indicators.