隔离的流动模式扩大了COVID-19传播中的社区差异

IF 1.4 Q2 SOCIAL SCIENCES, INTERDISCIPLINARY
Network Science Pub Date : 2023-04-17 DOI:10.1017/nws.2023.6
András György, Thomas Marlow, B. Abrahao, K. Makovi
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引用次数: 0

摘要

COVID-19的全球和不平衡传播反映在地方范围内,揭示了种族和民族界线上的明显差异。我们通过社区层面的流动性和病例数信息分析来应对了解这些不同结果的迫切需求。利用2020年芝加哥的数据,我们利用一个超人群易感-暴露-感染-去除模型,在邮政编码水平上重建和模拟SARS-CoV-2的传播。我们证明,暴露大多包含在自己的邮政编码和人口统计组。在此观察的基础上,我们说明,我们可以使用结合流动性和每次旅行所代表的风险的复合度量来理解流行病的进展,而单独这些因素无法解释观察到的邻里水平结果的异质性。在确定了这一结果之后,我们下一步将揭示这些因素的群体水平差异是如何导致不同种族和民族之间的发病率差异的。在此之后,我们提出了假设问题,以量化隔离如何通过改变流动模式影响COVID-19病例率。我们发现,流动网络中的隔离导致了不同人口群体的发病率不平等。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Segregated mobility patterns amplify neighborhood disparities in the spread of COVID-19
The global and uneven spread of COVID-19, mirrored at the local scale, reveals stark differences along racial and ethnic lines. We respond to the pressing need to understand these divergent outcomes via neighborhood level analysis of mobility and case count information. Using data from Chicago over 2020, we leverage a metapopulation Susceptible-Exposed-Infectious-Removed model to reconstruct and simulate the spread of SARS-CoV-2 at the ZIP Code level. We demonstrate that exposures are mostly contained within one’s own ZIP Code and demographic group. Building on this observation, we illustrate that we can understand epidemic progression using a composite metric combining the volume of mobility and the risk that each trip represents, while separately these factors fail to explain the observed heterogeneity in neighborhood level outcomes. Having established this result, we next uncover how group level differences in these factors give rise to disparities in case rates along racial and ethnic lines. Following this, we ask what-if questions to quantify how segregation impacts COVID-19 case rates via altering mobility patterns. We find that segregation in the mobility network has contributed to inequality in case rates across demographic groups.
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来源期刊
Network Science
Network Science SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
3.50
自引率
5.90%
发文量
24
期刊介绍: Network Science is an important journal for an important discipline - one using the network paradigm, focusing on actors and relational linkages, to inform research, methodology, and applications from many fields across the natural, social, engineering and informational sciences. Given growing understanding of the interconnectedness and globalization of the world, network methods are an increasingly recognized way to research aspects of modern society along with the individuals, organizations, and other actors within it. The discipline is ready for a comprehensive journal, open to papers from all relevant areas. Network Science is a defining work, shaping this discipline. The journal welcomes contributions from researchers in all areas working on network theory, methods, and data.
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