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美国呼吸道疾病的传染性差异。

Differential contagiousness of respiratory disease across the United States.

机构信息

Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545, USA; Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.

Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545, USA; Information Sciences Group, Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.

出版信息

Epidemics. 2023 Dec;45:100718. doi: 10.1016/j.epidem.2023.100718. Epub 2023 Sep 22.

DOI:10.1016/j.epidem.2023.100718
PMID:37757572
Abstract

The initial contagiousness of a communicable disease within a given population is quantified by the basic reproduction number, R. This number depends on both pathogen and population properties. On the basis of compartmental models that reproduce Coronavirus Disease 2019 (COVID-19) surveillance data, we used Bayesian inference and the next-generation matrix approach to estimate region-specific R values for 280 of 384 metropolitan statistical areas (MSAs) in the United States (US), which account for 95% of the US population living in urban areas and 82% of the total population. We focused on MSA populations after finding that these populations were more uniformly impacted by COVID-19 than state populations. Our maximum a posteriori (MAP) estimates for R range from 1.9 to 7.7 and quantify the relative susceptibilities of regional populations to spread of respiratory diseases. ONE-SENTENCE SUMMARY: Initial contagiousness of Coronavirus Disease 2019 varied over a 4-fold range across urban areas of the United States.

摘要

初始传染性疾病在特定人群中的传染性由基本繁殖数 R 来量化。该数字取决于病原体和人群特性。基于再现 2019 年冠状病毒病(COVID-19)监测数据的房室模型,我们使用贝叶斯推断和下一代矩阵方法,对美国 384 个大都市区(MSA)中的 280 个进行了特定区域 R 值的估计,这些 MSA 占美国居住在城市地区人口的 95%和总人口的 82%。我们专注于 MSA 人群,因为我们发现这些人群比州人群更受到 COVID-19 的均匀影响。我们的 R 的最大后验(MAP)估计值范围从 1.9 到 7.7,并量化了区域人群对呼吸道疾病传播的相对易感性。一句话总结:美国城市地区 2019 年冠状病毒病的初始传染性在 4 倍范围内变化。

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