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.

通过融合时间图神经网络和多模态数据，我们提出了一种名为 MGL4MEP 的新型框架，用于学习和预测流行病，通过利用特定的预训练语言模型和发现用户之间的潜在图结构，将大数据源（包括社交媒体内容）整合到流行病动态学习中，从而提供丰富的流行病动态指标。广泛的实验证明了我们框架在流行病预测和分析方面的有效性，相对于基准方法，在不同领域、流行病情况和预测时段上表现出色。时间图学习和多模态数据的融合使我们能够以更少的时间滞后、更便宜的成本和更多的潜在信息指标全面了解流行病景观。