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Early forecasting of the potential risk zones of COVID-19 in Chinas megacities

机译:对中国特大城市中COVID-19潜在危险区的早期预测

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摘要

Recently, the coronavirus disease 2019 (COVID-19) has become a worldwide public health threat. Early and quick identification of the potential risk zones of COVID-19 infection is increasingly vital for the megacities implementing targeted infection prevention and control measures. In this study, the communities with confirmed cases during January 21–February 27 were collected and considered as the specific epidemic data for Beijing, Guangzhou, and Shenzhen. We evaluated the spatiotemporal variations of the epidemics before utilizing the ecological niche models (ENM) to assemble the epidemic data and nine socioeconomic variables for identifying the potential risk zones of this infection in these megacities. Three megacities were differentiated by the spatial patterns and quantities of infected communities, average cases per community, the percentages of imported cases, as well as the potential risks, although their COVID-19 infection situations have been preliminarily contained to date. With higher risks that were predominated by various influencing factors in each megacity, the potential risk zones coverd about 75% to 100% of currently infected communities. Our results demonstrate that the ENM method was capable of being employed as an early forecasting tool for identifying the potential COVID-19 infection risk zones on a fine scale. We suggest that local hygienic authorities should keep their eyes on the epidemic in each megacity for sufficiently implementing and adjusting their interventions in the zones with more residents or probably crowded places. This study would provide useful clues for relevant hygienic departments making quick responses to increasingly severe epidemics in similar megacities in the world.
机译:最近,2019年冠状病毒病(COVID-19)已成为全球性的公共卫生威胁。对大城市实施有针对性的感染预防和控制措施,及早发现快速识别COVID-19感染的潜在危险区变得越来越重要。在本研究中,收集了1月21日至2月27日确诊病例的社区,并将其视为北京,广州和深圳的特定流行病数据。我们在利用生态位模型(ENM)收集流行病数据和九个社会经济变量以识别这些特大城市中这种感染的潜在危险区域之前,评估了流行病的时空变化。尽管迄今已初步控制了三个特大城市的COVID-19感染情况,但它们通过感染社区的空间模式和数量,每个社区的平均病例数,输入病例的百分比以及潜在风险来区分。在每个大城市中,较高的风险被各种影响因素所主导,因此潜在风险区覆盖了当前感染社区的约75%至100%。我们的结果表明,ENM方法能够用作早期预测工具,以在细微规模上识别潜在的COVID-19感染风险区。我们建议地方卫生当局应注意每个特大城市的流行病,以在更多居民或可能比较拥挤的地区充分实施和调整干预措施。这项研究将为相关卫生部门迅速应对世界上类似大城市中日益严重的流行病提供有用的线索。

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