Factors affecting COVID-19 infected and death rates inform lockdown-related policymaking.
Factors affecting COVID-19 infected and death rates inform lockdown-related policymaking.
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DOI：
10.1371/journal.pone.0241165
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发表时间：
2020
期刊：
影响因子：
3.7
通讯作者：
Ghosh P
中科院分区：
文献类型：
--
作者：
Roy S;Ghosh P
在夺走了全球近50万人的生命之后，新冠肺炎疫情丝毫没有减缓的迹象。虽然英国、美国、巴西和亚洲部分地区正在为第二波--或第一波的延伸--做准备，但当务之急是确定导致新冠肺炎感染和死亡人数的主要社会、经济、环境、人口、种族、文化和健康因素，以促进缓解和控制措施。我们处理了几个关于美国各州的开放获取数据集，以创建导致大流行传播的潜在因素的集成数据集。然后，我们应用几种有监督的机器学习方法来达成共识以及对关键因素进行排序。我们进行回归分析，找出影响封锁前感染和死亡率的关键因素，为今后制定封锁相关政策提供参考。人口密度、测试人数和机场交通是最具歧视性的因素，其次是较高年龄组(40岁以上，特别是60岁以上)。封锁后的感染率和死亡率受封锁前的影响很大，其次是人口密度和机场交通。虽然医疗保健指数似乎与死亡率无关，但对关键特征的主成分分析表明，有两组状态：(1)形成早期震中；(2)经历强烈的第二波或感染和死亡率较晚达到峰值。最后，对纽约市的一个小型案例研究表明，邻近行政区感染高峰期的天数与区域间流动性的相关性比区域间距离更大。形成早期热点的国家是机场或道路交通繁忙的地区，导致人类互动。美国人口密度和检测水平较高的州往往感染和死亡人数持续较高。死亡率似乎是由个人生理、先前存在的状况、年龄等驱动的，而不是性别、医疗机构或种族倾向。最后，关于封锁时间的决策应主要考虑封锁前的感染人数以及人口密度和机场交通。
After claiming nearly five hundred thousand lives globally, the COVID-19 pandemic is showing no signs of slowing down. While the UK, USA, Brazil and parts of Asia are bracing themselves for the second wave—or the extension of the first wave—it is imperative to identify the primary social, economic, environmental, demographic, ethnic, cultural and health factors contributing towards COVID-19 infection and mortality numbers to facilitate mitigation and control measures. We process several open-access datasets on US states to create an integrated dataset of potential factors leading to the pandemic spread. We then apply several supervised machine learning approaches to reach a consensus as well as rank the key factors. We carry out regression analysis to pinpoint the key pre-lockdown factors that affect post-lockdown infection and mortality, informing future lockdown-related policy making. Population density, testing numbers and airport traffic emerge as the most discriminatory factors, followed by higher age groups (above 40 and specifically 60+). Post-lockdown infected and death rates are highly influenced by their pre-lockdown counterparts, followed by population density and airport traffic. While healthcare index seems uncorrelated with mortality rate, principal component analysis on the key features show two groups: states (1) forming early epicenters and (2) experiencing strong second wave or peaking late in rate of infection and death. Finally, a small case study on New York City shows that days-to-peak for infection of neighboring boroughs correlate better with inter-zone mobility than the inter-zone distance. States forming the early hotspots are regions with high airport or road traffic resulting in human interaction. US states with high population density and testing tend to exhibit consistently high infected and death numbers. Mortality rate seems to be driven by individual physiology, preexisting condition, age etc., rather than gender, healthcare facility or ethnic predisposition. Finally, policymaking on the timing of lockdowns should primarily consider the pre-lockdown infected numbers along with population density and airport traffic.
影响因子：
15.1
作者：
Golestaneh, Ladan;Neugarten, Joel;Bellin, Eran
通讯作者：
Bellin, Eran
影响因子：
1.4
作者：
Gou, Jianping;Yi, Zhang;Xiong, Taisong
通讯作者：
Xiong, Taisong