RAPID: Infection Transmission of COVID19 in Urban Neighborhoods

尽管COVID-19大流行的大部分报道集中在国际和国家层面，但它也必然在地方层面展开。最近的报告显示，在许多美国城市，冠状病毒对历史上处于不利地位的社区产生了不成比例的影响。该项目将通过研究不同社区的居民在大流行期间保持社交距离的能力和倾向如何变化来确定感染率的差异。虽然大多数关于感染如何通过社区进行的研究都集中在国际传播上，但我们将这种理解扩展到它如何通过大都市地区社区的更精细的地理空间尺度进行。这项研究将提供关键的，可复制的见解，不仅是社会距离政策的有效性，但他们在减少社区感染率的公平性。灾害的后果往往受到一个人的生活（即，邻区效应）。流行病通过社区内部和社区之间的相互作用引入了网络扩散的额外成分。这项研究综合了这两个视角，以了解COVID 19感染如何在城市的社区传播，从而导致不同的感染率。该项目将利用波士顿居民现有的BEACON调查，对社交距离的做法和态度以及感染风险进行调查。调查结果将与手机生成的记录、诊断记录以及波士顿地区研究计划策划的社区地理特征列表中的社区流动进行交叉引用。该项目将测试一个由四部分组成的因果模型，其中邻里特征影响社会距离的做法，这反过来又塑造了城市的流动性，从而推动了感染的网络扩散。该奖项的结果将是至关重要的，因为世界各地的城市将继续管理当前的流行病，同时也为未来类似的危机做好准备。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

Though most coverage of the COVID-19 pandemic has focused on the international and national levels, it necessarily unfolds also at the local level. Recent reports reveal that in many American cities the coronavirus is having disproportionate impacts on neighborhoods that are historically disadvantaged. This project will identify disparities in infection rates by examining how residents of different neighborhoods have varied in their ability and tendency to practice social distancing during the pandemic. While most research on how infection proceeds through communities focuses on international transmission, we expand this understanding to how it progresses through the finer geospatial scale of communities in a metropolitan area. This research will provide critical, replicable insights for not only the effectiveness of social distancing policies, but their equitability in diminishing infection rates across communities.The consequences of disasters are often influenced by where one lives (i.e., neighborhood effects). A pandemic introduces the additional ingredient of network diffusion through interactions both within and between neighborhoods. This study synthesizes these two perspectives to understand how COVID19 infections spread through a city’s neighborhoods, leading to differential infection rates. The project will conduct a survey on social distancing practices and attitudes and exposure to infection leveraging the existing survey of Boston residents called BEACON. The survey responses will be cross-referenced with flows of movement across neighborhoods from cellphone-generated records; diagnosis records; and a list of neighborhood geographic features curated by the Boston Area Research Initiative. This project will enable tests of a four-part causal model, with neighborhood characteristics influencing social distancing practices, which in turn shape the mobility flows of the city, which then drive the network diffusion of infection. The results will be critical as cities around the world continue to manage the current pandemic while also preparing for similar crises in the future.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.