Carceral Amplification of COVID-19: Impacts for Community, Corrections Officer, and Incarcerated Population Risks

Abstract

COVID-19 is challenging many societal institutions, including our criminal justice systems. Some have proposed or enacted (e.g., the State of New Jersey) reductions in the jail and/or prison populations. We present a mathematical model to explore the epidemiologic impact of such interventions in jails and contrast them with the consequences of maintaining unaltered practices. We consider infection risk and likely in-custody deaths, and estimate how within-jail dynamics lead to spill-over risks, not only affecting incarcerated people but increasing exposure, infection, and death rates for both corrections officers and the broader community beyond the justice system. We show that, given a typical jail-community dynamic, operating in a business-as-usual way results in substantial, rapid, and ongoing loss of life. Our results are consistent with the hypothesis that large-scale reductions in arrest and speeding of releases are likely to save the lives of incarcerated people, jail staff, and the wider community.

FIGURE 1.

Schematic for a mathematical model of COVID-19 in a linked urban community-jail system. The population is represented in one of five possible compartments: S, E, I, needing M, and R. In addition, the population is divided into five distinct subpopulations: children under 18 years of age, elderly adults over 65 years of age, low-risk adults between 18 and 65, high-risk adults between 18 and 65 and jail staff (assumed to be between 18 and 65 years of age). Arrested adults move between the community, processing, and the court system and jail, while jail staff move between the community and jail. Children are assumed not to be eligible for arrest. E indicates exposed; I, infected; M, needing medical care; R, recovered/removed; S, susceptible.

FIGURE 2.

Schematic for a mathematical model of COVID-19 in a linked urban community-jail system with parameter annotations. Left: Depiction of model emphasizing movement between locations. Here “Court” stands for both processing and trials. Right: Depiction of the model emphasizing transition between disease states. The parameter family is shown for each type of transition. For example, although we show just one $\alpha$ between community and court, in our model, there are separate subscripted $\alpha$ parameters corresponding to different rates of arrest for different age groups. Similar logic applies to the other parameters. E indicates exposed; I, infected; M, needing medical care; R, recovered/removed; S, susceptible.

FIGURE 3.

Epidemic curves from a simulated COVID-19 epidemic in an urban community (right) and the connected population of persons in a jail (left). The curves demonstrate the expected magnitude and timing of the outbreak in the different populations, broken into the different etiologically relevant categories (i.e. exposed, infected, and hospitalized).

FIGURE 4.

Cumulative infections, hospitalizations, and deaths in the community (first row), among persons in jail (second row) and among jail staff (third row) for several combinations of incarceration deferment and accelerated release.

FIGURE 5.

Epidemic curves for the community (top panel), persons in jail (middle panel), and jail staff (bottom panel) under a shelter in place order as well as the deferment of bail-eligible persons. The curves show the impact of increased reduction in mixing (e.g., from the ability to physically distance persons in jail while in common areas) from baseline (dark blue) to identical to the community’s shelter-in-place order (green).