RAPID: Estimating the Reciprocal Relationship between COVID-19 Infections of Prisoners and Staff and Infections in the Surrounding Communities

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Controlling COVID-19 infections in prison is a critical part of “flattening the curve.” Prisons are high transmission and risk settings for the spread of infectious disease due to crowding, communal dining, and difficulty with sanitation. Prison staff may contribute to disease transmission between the local community and prisoners because staffers exit the prison and enter the community each shift (and vice-versa). This creates a bidirectional pathway for spreading the disease. Prisons, as well as other high density accommodations like nursing homes or cruise ships, are in immediate need of information concerning the spread of COVID-19. In particular, strategies are needed for halting or slowing the spread of COVID-19 in these settings. Prisons are a particularly difficult setting for such evaluative efforts because the current crisis leaves stakeholders little time to assess whether virus suppression strategies and their associated policies are working – creating a devastating information gap. Thus, quickly understanding the spread of COVID-19 both among people and across geographies, the effectiveness of policies and strategies for flattening the curve, as well as the return on investments (ROI) is critical for prisons to minimize and contain future outbreaks of COVID-19. This project will deepen our understanding of the reciprocal relationship between COVID-19 infections among prisoners, correctional staff and the communities where prisons are located. Findings will be useful to communities and congregate facility officials as they develop policies to manage these reciprocal infections in congregate settings, thus contributing to U. S. health and well-being. Prisons are at high risk for the spread of COVID-19, due to their communal settings, and the movement of staff between prisons and communities on a daily basis. To assess the dynamics of these risks, this project will develop a data driven dynamical disease model focusing on the temporal patterns in COVID-19 infections in the inter-connected prison/staff/community populations, and assess the relative efficacy of potential best practices for infection control, either aimed at the prisoner population and/or the staff population. In addition, the project will examine the reciprocal relationship between infections and deaths in prisons among prisoners and staff and the communities surrounding the prison from a geographic perspective. An ability to model the geospatial intricacies and the geodemographic impacts of this process is critical to developing a deeper understanding of how COVID-19 interacts with mobile (e.g., staff) and/or immobile (e.g., prisoner) populations. Specifically, the project will use geocomputational approaches for modeling the spatial envelopes of potential interactions between prison staff and their local residential communities. This includes the development of high-resolution spatiotemporal catchment areas to/from each federal prison in the United States. Finally, using return-on-investment (ROI) analysis, the project will illuminate the potential economic implications of COVID-19 infection control interventions targeting justice-involved populations. The anticipated insights from the ROI analysis include the identification of the key ROI drivers and the magnitude of impact required for the accrual of net savings to relevant decision-making agencies. Findings from the project will inform sociological theories regarding incarceration and literatures regarding reciprocal relationships between work and community.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.