Study population and setting
Authors used a Susceptible-Exposed-Infected-Medically treated-Removed model to describe the dynamics of COVID-19 transmission within the Allegheny County, PA corrections system, among incarcerated persons and their contacts. Authors segmented the model into five sub-populations (persons <18 years, low-risk adults, high-risk adults, elderly persons >65 years, and corrections personnel) and three transmission systems (the community, processing and court, and jail). Persons <18 years were only included in the community system, but could interact with sub-populations that were able to move within systems (e.g., a low risk adult moving from the community to processing and court). The model assumed that approximately 100 persons were arrested per day. Authors also included several interventions/policy changes (shelter-in-place orders, reductions in arrests, increases in rates of release, and changes to within-jail conditions) to estimate the impact these adaptations had on transmission dynamics within the three transmission systems.
Summary of Main Findings
The model estimated that an unmitigated outbreak would result in 926,108 infections, 51,497 hospitalizations, and 12,133 deaths in the community over the course of 180 days. The model estimated a more severe outbreak among incarcerated individuals: the 2,500 person-capacity jail was 0.2% the size of the greater community and individuals in this system experienced 4,949 infections, 264 hospitalizations, and 79 deaths. The peak was also much earlier in the jail system than the community system, occurring 31 days after the first infective case compared to 88 days after. Assuming shelter-in-place orders, infections, hospitalizations, and deaths in the community were considerably delayed and reduced (e.g., infections were reduced to 450,621). Conversely, within incarcerated populations, infections during the first half of the simulation (days 0-90) were similar to the unmitigated simulation, and the second half (days 91-180) were notably worse, resulting in a total of 7,421 infections over the course of the entire simulation. Reform within the jail system had greater impacts on incarcerated individuals, and to a lesser extent, jail personnel and the community. For example, discontinuing the arrests of bail-eligible individuals results in 22.9% and 3.9% reductions in infections among incarcerated individuals and the community, respectively. Discontinuing all low-level offense arrests resulted in 74.0%, 10.2%, and 19.7% reductions in infections among incarcerated individuals, jail personnel, and the community, respectively.
The model accounted for movement of populations between the different systems, and did not restrict movement to one direction. Authors calibrated the model based on the size and mixing patterns of each transmission system’s population.
Authors assumed that once patients were hospitalized, they did not spread COVID-19 any further. Although PPE worn by hospital staff limits ongoing transmission of COVID-19 in such settings, this may not always be true. Authors also assumed that all individuals leaving jail went straight back to the community, and did not account for other post-jail destinations, such as prison. However, authors argued that this is a small percentage of the population leaving jail and would not have a significant impact on results. The model used parameters specific to Allegheny County, PA (e.g., daily arrests, number of corrections personnel, size of jail population), so results may not be generalizable to locations with different population makeups.
Despite the United States having one of the highest incarceration rates in the world, most public models have excluded jails and other correctional facilities from their projections, potentially vastly underestimating the impact of COVID-19. This study is among the first to estimate the impact of COVID-19 on incarcerated populations, as well as on those with whom incarcerated individuals interact.
This review was posted on: 11 August 2020