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

控制监狱中的COVID-19感染是“拉平曲线”的关键部分。监狱是传染病传播的高传播和风险场所，原因是拥挤、集体用餐和卫生条件差。监狱工作人员可能助长当地社区与囚犯之间的疾病传播，因为工作人员每次轮班都要离开监狱进入社区（反之亦然）。这为疾病的传播创造了一个双向途径。监狱以及其他高密度住宿场所，如疗养院或游轮，迫切需要有关COVID-19传播的信息。特别是，需要采取战略来阻止或减缓COVID-19在这些环境中的传播。监狱是进行这种评估工作的一个特别困难的环境，因为目前的危机使利益攸关方几乎没有时间评估病毒抑制战略及其相关政策是否有效-造成了毁灭性的信息差距。因此，快速了解COVID-19在人群中和跨地区的传播，使曲线平坦化的政策和策略的有效性以及投资回报率（ROI）对于监狱最大限度地减少和遏制未来COVID-19的爆发至关重要。该项目将加深我们对囚犯、惩教人员和监狱所在社区感染COVID-19之间相互关系的理解。研究结果将有助于社区和聚集设施的官员，因为他们制定政策，以管理这些相互感染的聚集设置，从而有助于美国。S.健康和幸福。由于监狱的公共环境，以及监狱和社区之间的工作人员每天都在流动，因此监狱面临COVID-19传播的高风险。 为了评估这些风险的动态，该项目将开发一个数据驱动的动态疾病模型，重点关注相互关联的监狱/工作人员/社区人群中COVID-19感染的时间模式，并评估针对囚犯人群和/或工作人员人群的感染控制潜在最佳实践的相对功效。此外，该项目还将从地理角度审查监狱中囚犯和工作人员以及监狱周围社区之间的感染和死亡之间的相互关系。能够对地理空间的复杂性和这一过程的地理人口影响进行建模，对于深入了解COVID-19如何与移动的相互作用至关重要（例如，工作人员）和/或不动的（例如，囚犯）。具体而言，该项目将使用地理计算方法来模拟监狱工作人员与当地居民社区之间潜在互动的空间包络。这包括开发往返于美国每个联邦监狱的高分辨率时空集水区。最后，利用投资回报率（ROI）分析，该项目将阐明针对司法相关人群的COVID-19感染控制干预措施的潜在经济影响。投资回报率分析的预期洞察力包括确定关键投资回报率驱动因素，以及相关决策机构净节省累积所需的影响程度。该项目的研究结果将为监禁方面的社会学理论和工作与社区之间相互关系的文献提供信息。该奖项反映了NSF的法定使命，并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，被认为值得支持。

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.