The Epidemiological Implications of Incarceration Dynamics in Jails for Community, Corrections Officer, and Incarcerated Population Risks from COVID-19

Eric Lofgren; Kristian Lum; Aaron Horowitz; Brooke Madubuowu; Nina Fefferman

Introduction 1 people to practice CDC recommendations such as social distancing [12] , the 23 incarcerated population has a higher expected rate of existing health conditions than 24 the community from which they come [13] [14] [15] , jails are dependent completely on a 25 workforce that moves in and out of the jail and the community including vendors, 26 lawyers, corrections officers, medical staff, etc., and there is strong evidence that 27 incarceration itself has profound adverse effects on the health of incarcerated 28 people [16] [17] [18] . These descriptors make jails highly likely not only to place detained 29 people at increased risk of infection and resulting severe outcomes, but also to function 30 as a driver for increased infectivity, adversely impacting attempts to contain and 31 mitigate disease spread in the broader communities in which jails are located. To study 32 the dynamics of this system and provide quantitative metrics for risk to incarcerated 33 populations and the populations with which incarcerated people necessarily interact, we 34 construct and tailor a epidemiological model of COVID-19 transmission, and then use 35 that model to consider how some possible reforms to the system (i.e. reduction in arrest 36 intake, increased rates of returning incarcerated people to their homes, and 37 improvement of conditions within the jails) will alter these baseline risks. 38 Model/Methods 39 Transmission Model 40 We begin by tailoring a standard SEIR model to the specific dynamics of COVID-19. 41 We first split our total population into four categories of risk: Children under 18 42 (denoted with the subscript K), Low-risk adults (denoted with the subscript L), 43 High-risk adults (denoted with the subscript H), and Elderly adults (denoted with the 44 subscript E). We also designate a separate population category for jail staff, O (note: consider death from any non-COVID-19 cause; this is done to highlight the 58 COVID-19-specific dynamics. Additionally, as a simplifying assumption due to their low 59 rates of both infections and complications, we do not model hospitalizations or deaths 60 in children. Similarly, once hospitalized, patients are assumed not to spread COVID-19 61 further, as additionally modeling the impact of healthcare-associated COVID-19 cases is 62 well beyond the scope of this model. Lastly, we split our population into segments 63 depending on the subsection of the community or jail system in which they are 64 currently functioning: the community at large, C, the processing system for the jail, P , 65 the court system T , and the jail system, J.