Estimation of COVID-19 spread curves integrating global data and borrowing information
Estimation of COVID-19 spread curves integrating global data and borrowing information
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DOI：
10.1371/journal.pone.0236860
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发表时间：
2020-07-29
期刊：
影响因子：
3.7
通讯作者：
Mallick, Bani
中科院分区：
文献类型：
--
作者：
Lee, Se Yoon;Lei, Bowen;Mallick, Bani
目前，新型冠状病毒病2019年(新冠肺炎)正对全球健康构成巨大威胁。病毒的快速传播造成了大流行，世界各国都在努力应对新冠肺炎感染病例激增的问题。目前还没有美国食品和药物管理局批准的药物或其他疗法来预防或治疗新冠肺炎：有关这种疾病的信息非常有限，而且即使存在，也是分散的。这促进了数据集成的使用，将来自不同来源的数据组合在一起，并以统一的视图获取有用的信息。在本文中，我们提出了一种贝叶斯分层模型，该模型集成了全球数据，用于实时预测多个国家的感染轨迹。由于拟议的模型利用了跨多个国家借阅信息的优势，它的表现优于现有的基于单个国家的模型。由于完全采用了贝叶斯方法，该模型提供了一个强大的预测工具，赋予了不确定性量化的能力。此外，联合变量选择技术已被整合到拟议的建模方案中，该方案旨在确定可能的国家一级因新冠肺炎导致的严重疾病的风险因素。
Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to global health. The rapid spread of the virus has created pandemic, and countries all over the world are struggling with a surge in COVID-19 infected cases. There are no drugs or other therapeutics approved by the US Food and Drug Administration to prevent or treat COVID-19: information on the disease is very limited and scattered even if it exists. This motivates the use of data integration, combining data from diverse sources and eliciting useful information with a unified view of them. In this paper, we propose a Bayesian hierarchical model that integrates global data for real-time prediction of infection trajectory for multiple countries. Because the proposed model takes advantage of borrowing information across multiple countries, it outperforms an existing individual country-based model. As fully Bayesian way has been adopted, the model provides a powerful predictive tool endowed with uncertainty quantification. Additionally, a joint variable selection technique has been integrated into the proposed modeling scheme, which aimed to identify possible country-level risk factors for severe disease due to COVID-19.