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社区场所新冠疫情暴露风险估算器。

Community venue exposure risk estimator for the COVID-19 pandemic.

机构信息

Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USA; Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA, USA.

Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA.

出版信息

Health Place. 2020 Nov;66:102450. doi: 10.1016/j.healthplace.2020.102450. Epub 2020 Sep 29.

Abstract

Complexities of virus genotypes and the stochastic contacts in human society create a big challenge for estimating the potential risks of exposure to a widely spreading virus such as COVID-19. To increase public awareness of exposure risks in daily activities, we propose a birthday-paradox-based probability model to implement in a web-based system, named COSRE (community social risk estimator) and make in-time community exposure risk estimation during the ongoing COVID-19 pandemic. We define exposure risk to mean the probability of people meeting potential cases in public places such as grocery stores, gyms, libraries, restaurants, coffee shops, offices, etc. Our model has three inputs: the real-time number of active and asymptomatic cases, the population in local communities, and the customer counts in the room. With COSRE, possible impacts of the pandemic can be explored through spatiotemporal analysis, e.g., a variable number of people may be projected into public places through time to assess changes of risk as the pandemic unfolds. The system has potential to advance understanding of the true exposure risks in various communities. It introduces an objective element to plan, prepare and respond during a pandemic. Spatial analysis tools are used to draw county-level exposure risks of the United States from April 1 to July 15, 2020. The correlation experiment with the new cases in the next two weeks shows that the risk estimation model offers promise in assisting people to be more precise about their personal safety and control of daily routine and social interaction. It can inform business and municipal COVID-19 policy to accelerate recovery.

摘要

病毒基因型的复杂性和人类社会中的随机接触给评估广泛传播的病毒(如 COVID-19)的潜在暴露风险带来了巨大挑战。为了提高公众对日常活动中暴露风险的认识,我们提出了一种基于生日悖论的概率模型,并在一个名为 COSRE(社区社会风险估计器)的网络系统中实现,以便在当前 COVID-19 大流行期间及时进行社区暴露风险估计。我们将暴露风险定义为人们在杂货店、健身房、图书馆、餐馆、咖啡店、办公室等公共场所遇到潜在病例的概率。我们的模型有三个输入:活跃病例和无症状病例的实时数量、当地社区的人口以及房间内的顾客数量。通过 COSRE,可以通过时空分析探索大流行的可能影响,例如,通过时间将不同数量的人投射到公共场所,以评估随着大流行的发展风险的变化。该系统有可能深入了解各个社区的真实暴露风险。它为大流行期间的规划、准备和应对引入了一个客观因素。该系统使用空间分析工具从 2020 年 4 月 1 日至 7 月 15 日绘制了美国县级暴露风险图。与未来两周新增病例的相关实验表明,风险估计模型在帮助人们更准确地了解个人安全以及控制日常活动和社会互动方面具有潜力。它可以为企业和市政 COVID-19 政策提供信息,以加速恢复。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c814/7522786/97a8e153d134/gr1a_lrg.jpg

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