融合基因、行为与公共卫生数据，提升新冠病例激增预测能力
Investigating the effectiveness of multimodal data in forecasting SARS-COV-2 case surges
- 整合突变、移动轨迹和社交媒体等多模态数据进行预测
- 不同国家和数据类型间预测效果差异显著
- 适合需本地化疫情预警的公共卫生机构参考
新冠疫情应对高度依赖统计与机器学习模型来预测病例数量和死亡率等关键指标，这些预测对及时实施公共卫生干预、阻断传播链至关重要。尽管现有模型主要基于传统流行病学数据，但基因信息与人类行为等替代数据集的潜力仍待挖掘。本研究探讨了多种特征模态在预测国家层面病例激增中的有效性。结果表明，生物特征（如病毒突变）、公共卫生数据（如病例数、防控政策）以及人类行为特征（如移动性、社交媒体讨论）均具有预测价值。值得注意的是，各国及不同数据模态间的预测性能存在明显异质性，提示激增预测模型需适配特定国家背景与疫情阶段。总体而言，本研究强调将非传统数据源纳入现有疾病监测体系，有助于提升对疫情动态的预测能力。
原文摘要 · Abstract (English)
The COVID-19 pandemic response relied heavily on statistical and machine learning models to predict key outcomes such as case prevalence and fatality rates. These predictions were instrumental in enabling timely public health interventions that helped break transmission cycles. While most existing models are grounded in traditional epidemiological data, the potential of alternative datasets, such as those derived from genomic information and human behavior, remains underexplored. In the current study, we investigated the usefulness of diverse modalities of feature sets in predicting case surges. Our results highlight the relative effectiveness of biological (e.g., mutations), public health (e.g., case counts, policy interventions) and human behavioral features (e.g., mobility and social media conversations) in predicting country-level case surges. Importantly, we uncover considerable heterogeneity in predictive performance across countries and feature modalities, suggesting that surge prediction models may need to be tailored to specific national contexts and pandemic phases. Overall, our work highlights the value of integrating alternative data sources into existing disease surveillance frameworks to enhance the prediction of pandemic dynamics.
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