Althouse Benjamin M, Wallace Brendan, Case Brendan, Scarpino Samuel V, Allard Antoine, Berdahl Andrew M, White Easton R, Hebert-Dufresné Laurent
Institute for Disease Modeling, Global Health, Bill & Melinda Gates Foundation, Seattle, WA.
University of Washington, Seattle, WA 98105.
medRxiv. 2020 Oct 28:2020.08.21.20179473. doi: 10.1101/2020.08.21.20179473.
Controlling the spread of COVID-19 - even after a licensed vaccine is available - requires the effective use of non-pharmaceutical interventions, e.g., physical distancing, limits on group sizes, mask wearing, etc. To date, such interventions have not been uniformly and/or systematically implemented across the United States of America (US). For example, even when under strict stay-at-home orders, numerous jurisdictions in the US granted exceptions and/or were in close proximity to locations with entirely different regulations in place. Here, we investigate the impact of such geographic inconsistencies in epidemic control policies by coupling high-resolution mobility, search, and COVID case data to a mathematical model of SARS-CoV-2 transmission. Our results show that while stay-at-home orders decrease contacts in most areas of the US, some specific activities and venues often see an increase in attendance. As an example, over the month of March 2020, between 10 and 30% of churches in the US saw increases in attendance; even as the total number of visits to churches declined nationally. This heterogeneity, where certain venues see substantial increases in attendance while others close, suggests that closure can cause individuals to find an open venue, even if that requires longer-distance travel. And, indeed, the average distance travelled to churches in the US rose by 13% over the same period, and over the summer, churches with more than 50 average weekly visitors saw an increase of 81% in distance visitors had to travel to attend. Strikingly, our mathematical model reveals that, across a broad range of model parameters, partial measures can often be worse than no measures at all. In the most severe cases, individuals not complying with policies by traveling to neighboring jurisdictions can create epidemics when the outbreak would otherwise have been contained. Indeed, using county-level COVID-19 data, we show that mobility from high-incidence to low-incidence associated with travel for venues like churches, parks, and gyms consistently precedes rising case numbers in the low-incidence counties. Taken together, our data analysis of nearly 120 million church visitors across 184,677 churches, 14 million grocery visitors across 7,662 grocery stores, 13.5 million gym visitors across 5,483 gyms, 7.7 million cases across 3,195 counties, and modeling results highlight the potential unintended consequences of inconsistent epidemic control policies and stress the importance of balancing the societal needs of a population with the risk of an outbreak growing into a large epidemic, and the urgent need for centralized implementation and enforcement of non-pharmaceutical interventions.
控制新冠病毒病(COVID-19)的传播——即使在有许可疫苗可用之后——仍需要有效利用非药物干预措施,例如保持身体距离、限制群体规模、佩戴口罩等。迄今为止,此类干预措施在美国尚未得到统一和/或系统的实施。例如,即使在严格的居家令之下,美国众多司法管辖区仍给予豁免和/或与实施完全不同规定的地区相邻。在此,我们通过将高分辨率的出行、搜索和新冠病例数据与严重急性呼吸综合征冠状病毒2(SARS-CoV-2)传播的数学模型相结合,研究这种疫情防控政策中的地理不一致性的影响。我们的结果表明,虽然居家令在美国大部分地区减少了接触,但一些特定活动和场所的参与人数却经常增加。例如,在2020年3月期间,美国10%至30%的教堂参与人数增加;即使全国范围内教堂的总访问量下降。这种异质性,即某些场所的参与人数大幅增加而其他场所关闭,表明关闭可能会导致个人寻找开放的场所,即使这需要更远距离的出行。事实上,同期美国前往教堂的平均出行距离增加了13%,并且在夏季,平均每周有超过50名访客的教堂,访客前往教堂的出行距离增加了81%。引人注目的是,我们的数学模型表明,在广泛的模型参数范围内,部分措施往往可能比完全不采取措施更糟糕。在最严重的情况下,不遵守政策前往邻近司法管辖区的个人可能会引发疫情,而原本疫情是可以得到控制的。事实上,利用县级新冠病毒病数据,我们表明与前往教堂、公园和健身房等场所出行相关的从高发病率地区到低发病率地区的人员流动,始终先于低发病率县病例数的上升。综合来看,我们对184,677座教堂的近1.2亿名教堂访客、7,662家杂货店的1400万名杂货店访客、5,483家健身房的1350万名健身房访客、3,195个县的770万例病例的数据分析以及建模结果,凸显了不一致的疫情防控政策可能产生的意外后果,并强调了平衡人群的社会需求与疫情爆发演变为大规模疫情风险的重要性,以及集中实施和执行非药物干预措施的迫切需求。