Forecasting Emerging Pandemics with Transfer Learning and Location-aware News Analysis
Forecasting Emerging Pandemics with Transfer Learning and Location-aware News Analysis
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DOI：
10.1109/bigdata55660.2022.10020218
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Jing Chen;Germán G. Creamer;Yue Ning
中科院分区：
文献类型：
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作者：
Jing Chen;Germán G. Creamer;Yue Ning
监测和预测流行病对公共卫生组织和决策者采取适当措施和调整预防策略至关重要。对于遏制新冠肺炎等新兴疫情的传播，早期预测尤为重要。然而，尽管加强了对各种流行病的研究和开发，但仍有几个挑战尚未解决。一方面，由于数据匮乏和经验不足，对新发疾病的早期疫情预测很困难。另一方面，许多现有的研究忽视或未能充分利用新闻、地理位置和气候等社会因素的贡献。尽管一些研究人员已经认识到社会特征的深刻影响，但捕捉这些特征与流行病之间的动态关联需要对不同格式的数据和机制有广泛的理解。在本文中，我们设计了一种神经传递学习结构TLSS，用于学习和传递现有流行病的一般特征，以预测新的流行病。我们提出了一个新的特征模块来学习新闻情感和语义信息对疫情传播的影响。然后，我们将这些信息与历史时间序列特征结合起来，在动态传播过程中预测未来的感染病例。我们将该模型与流行预测中的几种最新统计方法和深度学习方法在不同基本事实的提前期下进行了比较。我们对新冠肺炎在美国发展的三个阶段进行了广泛的实验。我们的实验表明，我们的方法对COVID感染病例具有很强的预测性能，特别是在准备时间较长的情况下。
Monitoring and forecasting epidemic diseases are of prime importance to public health organizations and policymakers in taking proper measures and adjusting prevention tactics. Early prediction is especially important to restrict the spread of emerging pandemics such as COVID-19. However, despite increasing research and development for various epidemics, several challenges remain unresolved. On the one hand, early-stage epidemic prediction for emerging new diseases is difficult because of data paucity and lack of experience. On the other hand, many existing studies ignore or fail to leverage the contribution of social factors such as news, geolocations, and climate. Even though some researchers have recognized the profound impact of social features, capturing the dynamic correlation between these features and pandemics requires an extensive understanding of heterogeneous formats of data and mechanisms. In this paper, we design TLSS, a neural transfer learning architecture for learning and transferring general characteristics of existing epidemic diseases to predict a new pandemic. We propose a new feature module to learn the impact of news sentiment and semantic information on epidemic transmission. We then combine this information with historical time-series features to forecast future infection cases in a dynamic propagation process. We compare the proposed model with several state-of-the-art statistics approaches and deep learning methods in epidemic prediction with different lead times of ground truth. We conducted extensive experiments on three stages of COVID-19 development in the United States. Our experiment demonstrates that our approach has strong predictive performance for COVID infection cases, especially with longer lead times.