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Accurate forecasting and analysis of emerging pandemics play a crucial rolein effective public health management and decision-making. Traditionalapproaches primarily rely on epidemiological data, overlooking other valuablesources of information that could act as sensors or indicators of pandemicpatterns. In this paper, we propose a novel framework called MGL4MEP thatintegrates temporal graph neural networks and multi-modal data for learning andforecasting. We incorporate big data sources, including social media content,by utilizing specific pre-trained language models and discovering theunderlying graph structure among users. This integration provides richindicators of pandemic dynamics through learning with temporal graph neuralnetworks. Extensive experiments demonstrate the effectiveness of our frameworkin pandemic forecasting and analysis, outperforming baseline methods acrossdifferent areas, pandemic situations, and prediction horizons. The fusion oftemporal graph learning and multi-modal data enables a comprehensiveunderstanding of the pandemic landscape with less time lag, cheap cost, andmore potential information indicators.

Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data