![]() Be able to maintain distancing outside of one’s household is dependent upon a variety of factors, including living environments (apartments versus single-family dwellings), settings of physical infrastructures (public transportation in inner cities versus automobiles in suburbs), and work arrangements (IT consultants working at home versus employees in food service industry). is impractical in some Asian cities such as Tokyo and Hong Kong due to the high overall population density levels. For instance, the 2-meter separation adopted in the U.S. ![]() Different specific distancing guidelines are required for different localities to accommodate the local geographical contexts. Thus, distancing is not a foolproof to avoid contracting the virus, although it is still strongly recommended. Increasing evidence suggested that not just droplets, but aerosolization of viral particles is a possible transmission mechanism. With no certainty on the details of the transmission processes, social distancing, which is a misnomer and should be replaced by physical distancing or separation, has been regarded as one of the effective means to combat the spread of virus. Despite the lack of this critical knowledge, numerous studies, published and on-going, try to develop models to better predict various disease statistics, such as the reproduction number (R t), but ultimately to predict the size of the infected population and causalities. In the midst of the COVID-19 pandemic, we are still uncertain about the pathways how one may contract the virus. 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. 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. Adding the three population subgroup percentage variables raised the R-squared of the aspatial models to 72% and the spatial model to 84%. Population density alone accounts for 57% of the variation (R-squared) in the aspatial models and up to 76% in the spatial models. Spatial regression models with a spatial error specification are also used to account for the spatial spillover effect. Additional variables reflecting the percentages of African Americans, Hispanic-Latina, and older adults in logarithmic scale are also included. 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. Daily cumulative cases by counties are converted into 7-day moving averages. This study shows that population density is an effective predictor of cumulative infection cases in the U.S. do not include population density explicitly. Most models developed to predict the spread of COVID-19 in the U.S. How far people can be spatially separated is partly behavioral but partly constrained by population density. 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.
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