Identifying and evaluating prevention strategies for COVID-19 in correctional facilities

PROJECT SUMMARY Respiratory infections, including SARS-CoV-2, disproportionately affect residents of correctional facilities (jails and prisons). While the Federal Bureau of Prisons and state Departments of Correction (DOCs) implemented numerous prevention strategies including social distancing protocols, vaccination campaigns, and testing programs to mitigate transmission and reduce the disease burden, overarching guidance on COVID-19 prevention within correctional facilities is limited. As a result, DOCs must develop and modify their policies based on existing evidence regarding the effectiveness of COVID-19 prevention strategies within correctional facilities. Unfortunately, the existing evidence base is limited. Specifically, the effectiveness of prevention strategies has principally been estimated in isolation (not in combination with other strategies) and while holding the variant constant. Because of this, DOCs are left with little evidence on how to implement and adapt prevention strategies in combination and under the ever-changing COVID-19 landscape. With the goal of expanding the evidence base for infectious disease prevention strategies within correctional facilities, we will estimate the effects of testing and vaccination on the burden of COVID-19 in both jails and prisons. To do so, we will develop an individual level discrete time hazard (transmission) model of SARS-CoV-2 and test the feasibility and reliability of a cutting-edge statistical causal inference approach as an alternative to transmission modeling (Aims 1 & 2). To examine the effects of testing and vaccination in combination and to identify scenarios when strategies require modification to contain spread and reduce disease burden, we will simulate waves of SARS-CoV-2 in the community and identify the strategy combinations required to prevent outbreaks within facilities using our transmission models (Aim 3). The proposed simulation approach will allow for the simulation of SARS-CoV-2 transmission and disease under known and future, theoretical scenarios. The execution of the proposed aims will strengthen the evidence available to DOCs and other policymakers and could make possible the estimation of indirect treatment effects under a causal framework within complex, nested social networks. In addition, their execution, coupled with the proposed training program comprising coursework, structured mentoring, and experiential learning, will allow Dr. Lind (the candidate) to enrich her knowledge of infectious disease transmission modeling, causal inference methods for treatment effect estimation in the presence of interference, and health disparities and infection control within a highly marginalized population, residents of correctional facilities. The candidate has established an expert mentoring and advisory team led by Dr. Albert Ko at the Epidemiology of Microbial Diseases Department at the Yale School of Public Health to enable this training, guide Dr. Lind's transition to independence during the R00 award phase and support her growth as an independently funded infectious disease epidemiologist.