Diverse local epidemics reveal the distinct effects of population density, demographics, climate, depletion of susceptibles, and intervention in the first wave of COVID-19 in the United States

N. Afshordi, Benjamin P. Holder, Mohammad Bahrami, Daniel Lichtblau
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引用次数: 15

Abstract

The SARS-CoV-2 pandemic has caused significant mortality and morbidity worldwide, sparing almost no community. As the disease will likely remain a threat for years to come, an understanding of the precise influences of human demographics and settlement, as well as the dynamic factors of climate, susceptible depletion, and intervention, on the spread of localized epidemics will be vital for mounting an effective response. We consider the entire set of local epidemics in the United States; a broad selection of demographic, population density, and climate factors; and local mobility data, tracking social distancing interventions, to determine the key factors driving the spread and containment of the virus. Assuming first a linear model for the rate of exponential growth (or decay) in cases/mortality, we find that population-weighted density, humidity, and median age dominate the dynamics of growth and decline, once interventions are accounted for. A focus on distinct metropolitan areas suggests that some locales benefited from the timing of a nearly simultaneous nationwide shutdown, and/or the regional climate conditions in mid-March; while others suffered significant outbreaks prior to intervention. Using a first-principles model of the infection spread, we then develop predictions for the impact of the relaxation of social distancing and local climate conditions. A few regions, where a significant fraction of the population was infected, show evidence that the epidemic has partially resolved via depletion of the susceptible population (i.e., "herd immunity"), while most regions in the United States remain overwhelmingly susceptible. These results will be important for optimal management of intervention strategies, which can be facilitated using our online dashboard.
不同的地方流行病揭示了人口密度、人口结构、气候、易感人群的消耗和对美国第一波COVID-19的干预的不同影响
SARS-CoV-2大流行在世界范围内造成了严重的死亡率和发病率,几乎没有一个社区幸免。由于该疾病可能在未来数年仍将是一种威胁,因此,了解人口统计和住区以及气候、易感耗竭和干预等动态因素对局部流行病传播的确切影响,对于采取有效应对措施至关重要。我们考虑了美国所有的地方流行病;人口、人口密度和气候因素的广泛选择;以及当地流动数据,跟踪社会距离干预措施,以确定推动病毒传播和遏制的关键因素。首先假设病例/死亡率指数增长率(或衰减率)的线性模型,我们发现,一旦考虑到干预措施,人口加权密度、湿度和中位年龄主导了增长和下降的动态。对不同大都市地区的关注表明,一些地区受益于3月中旬几乎同时发生的全国停摆和/或地区气候条件;而另一些国家则在干预之前遭受了严重的疫情。然后,利用感染传播的第一原理模型,我们对放松社交距离和当地气候条件的影响进行了预测。在少数几个地区,有相当一部分人口受到感染,有证据表明,通过减少易感人口(即"群体免疫"),该流行病已得到部分解决,而美国的大多数地区仍然非常易感。这些结果对于干预策略的最佳管理非常重要,这可以使用我们的在线仪表板来促进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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