Department of Geography and GeoInformation Science, George Mason University, Fairfax, VA, United States of America.
NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, United States of America.
PLoS One. 2020 Dec 23;15(12):e0242398. doi: 10.1371/journal.pone.0242398. eCollection 2020.
Physical distancing has been argued as one of the effective means to combat the spread of COVID-19 before a vaccine or therapeutic drug becomes available. How far people can be spatially separated is partly behavioral but partly constrained by population density. Most models developed to predict the spread of COVID-19 in the U.S. do not include population density explicitly. This study shows that population density is an effective predictor of cumulative infection cases in the U.S. at the county level. Daily cumulative cases by counties are converted into 7-day moving averages. Treating the weekly averages as the dependent variable and the county population density levels as the explanatory variable, both in logarithmic scale, this study assesses how population density has shaped the distributions of infection cases across the U.S. from early March to late May, 2020. Additional variables reflecting the percentages of African Americans, Hispanic-Latina, and older adults in logarithmic scale are also included. Spatial regression models with a spatial error specification are also used to account for the spatial spillover effect. Population density alone accounts for 57% of the variation (R-squared) in the aspatial models and up to 76% in the spatial models. Adding the three population subgroup percentage variables raised the R-squared of the aspatial models to 72% and the spatial model to 84%. The influences of the three population subgroups were substantial, but changed over time, while the contributions of population density have been quite stable after the first several weeks, ascertaining the importance of population density in shaping the spread of infection in individual counties, and in their neighboring counties. Thus, population density and sizes of vulnerable population subgroups should be explicitly included in transmission models that predict the impacts of COVID-19, particularly at the sub-county level.
身体距离被认为是在疫苗或治疗药物可用之前对抗 COVID-19 传播的有效手段之一。人们可以在多大程度上在空间上分开，部分是行为上的，但部分受到人口密度的限制。大多数开发来预测 COVID-19 在美国传播的模型并没有明确包括人口密度。本研究表明，人口密度是美国县级累计感染病例的有效预测指标。将各县的每日累计病例转换为 7 天移动平均值。将每周平均值视为因变量，将县人口密度水平视为对数尺度上的解释变量，本研究评估了人口密度如何从 2020 年 3 月初到 5 月底塑造了美国各地感染病例的分布。还包括反映对数尺度上非裔美国人、西班牙裔-拉丁裔和老年人百分比的其他变量。具有空间误差指定的空间回归模型也用于解释空间溢出效应。人口密度单独解释了非空间模型中 57%的变化（R 平方），在空间模型中高达 76%。添加三个人口亚组百分比变量后，非空间模型的 R 平方增加到 72%，空间模型增加到 84%。这三个人口亚组的影响是巨大的，但随着时间的推移而变化，而人口密度的贡献在最初几周后相当稳定，这证实了人口密度在塑造个别县和其相邻县感染传播中的重要性。因此，在预测 COVID-19 影响的传播模型中，应明确包括人口密度和脆弱人口亚组的规模。
