Controlling COVID-19 outbreaks in the correctional setting: A mathematical modelling study

Abstract

Correctional centres (termed here 'prisons') are at high risk of COVID-19 and have featured major outbreaks worldwide. Inevitable close contacts, frequent inmate movements, and a disproportionate burden of co-morbidities mean these environments need to be prioritised in any public health response to respiratory pathogens such as COVID-19. We developed an individual-based SARS-CoV-2 transmission model for the prison system in New South Wales, Australia - incorporating all 33 correctional centres, 13,458 inmates, 578 healthcare and 6,909 custodial staff. Potential COVID-19 disease outbreaks were assessed under various mitigation strategies, including quarantine on entry, isolation of cases, rapid antigen testing of staff, as well as immunisation.Without control measures, the model projected a peak of 472 new infections daily by day 35 across the prison system, with all inmates infected by day 120. The most effective individual mitigation strategies were high immunisation coverage and prompt lockdown of centres with infected inmates which reduced outbreak size by 62-73%. Other than immunisation, the combination of quarantine of inmates at entry, isolation of proven or suspected cases, and widespread use of personal protective equipment by staff and inmates was the most effective strategy. High immunisation coverage mitigates the spread of COVID-19 within and between correctional settings but is insufficient alone. Maintaining quarantine and isolation, along with high immunisation levels, will allow correctional systems to function with a low risk of outbreaks. These results have informed public health policy for respiratory pathogens in Australian correctional systems.

Fig 1. Structure of the model.

The model represents the NSW prison system consisting of prisons with varying security settings. Each prison consists of areas, which consists of units, which consists of cells. The model considers the possibility transfers between prisons, as well as visits to 38 courts via 20 transfer buses.

Fig 2. Schematic diagram of the model showing COVID-19 disease states and progression.

Fig 3. Simulation results according to SARS-CoV-2 variants and type of individual.

Panel A shows a comparison of the number of new cases based on SARS-CoV-2 alpha, delta, and omicron strain transmission probabilities. Panel B shows a comparison of the number of cumulative cases and deaths using three COVID-19 variants. Panel C shows a comparison of the number of new cases given different entry points for the virus.

Fig 4. Simulation results using face mask, rapid antigen testing, and movement restriction strategies.

Panel A shows a comparison of the number of new cases using various strategies involving face masks. Panel B shows a comparison of the number of new cases using different entry points for the virus and modifications in rapid antigen testing. (2 lines superimposed) Panel C shows a comparison of the number of new cases using various strategies related to movement.

Fig 5. Simulation results using isolation and lockdown strategies.

Simulation results according to Panel A shows a comparison of the number of new cases using various lockdown strategies. Panel B shows a comparison of the number of new cases using various delays in the implementation of prison lockdown.

Fig 6. Comparison of the number of new cases using various levels of immunisation coverage.

Fig 7. System-wide outbreak probabilities and system-wide outbreak magnitude for varied control strategies.

A prison outbreak was defined as >5 infections per prison: a system/wide out 2 or more prisons that meet the prison outbreak criteria. The high coverage vaccination + quarantine strategy did not result to any secondary infections based on 1 infected new inmate over 100 simulations.