Award Abstract #2032384

RAPID: Infection Transmission of COVID19 in Urban Neighborhoods

See grant description on NSF site

Program Manager:

Scott Freundschuh

Active Dates:

Awarded Amount:



Daniel O'Brien

J. Lee Hargraves

Qi Wang

Alina Ristea

Russell K Schutt

Awardee Organization:

Northeastern University

Funder Divisions:

Social Behavioral and Economic Sciences (SBE)


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.

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