Decarceration and COVID-19 infections in U.S. Immigration and Customs Enforcement detention facilities: a simulation modeling study

Abstract

Background: U.S. Immigration and Customs Enforcement (ICE) facilities had high rates of COVID-19 infections and mortality during the global pandemic. We sought to quantify how many COVID-19 infections could have been averted through different decarceration strategies.

Methods: We developed a set of stochastic simulation models of SARS-CoV-2 transmission in ICE facilities. Employing incremental mixture importance sampling (IMIS), we calibrated them to empirical targets derived from publicly available case time series for ICE facilities, and publicly available facility population censuses prior to vaccine availability (May 6, 2020 to December 31, 2020). The models included infection importation from extra-facility sources. We evaluated reduction of the incarcerated population by 10-90%. People who were decarcerated faced background cumulative risks of infection and detection based on a weighted average of county-level estimates from the covidestim model, which is a Bayesian evidence synthesis model.

Findings: Without decarceration, the infection rate was 5.05 per 1000 person-days (95% CrI 3.40-6.81) and case rate was 1.53 per 1000 person-days (95% CrI 1.04-2.02). Rates declined linearly when decarceration did not reduce contacts of people remaining in facilities and faster than linearly when it did reduce contacts. At all decarceration levels, rates were substantially higher when contacts were not reduced. Even with 90% decarceration, infection rates for people remaining in facilities were higher than or comparable to otherwise similar free-living people.

Interpretation: The decline in COVID-19 infection rates with decarceration was linear or faster than linear depending on how decarceration was implemented. Our findings highlight infection risks associated with incarceration, which compound other health harms of incarceration.

Funding: Stanford's COVID-19 Emergency Response Fund; the National Institute on Drug Abuse; and the National Institute of Mental Health.

Keywords: COVID-19; Immigration detention; Incarceration; Simulation modeling.

Fig. 1

Model schematic. We constructed a set of stochastic SEIDR compartmental models of SARS-CoV-2 transmission with one per ICE facility within the ICE system. Shown here is the structure of one such model. There were five health states including: susceptible (S), exposed (E), infected (I), detected (D), and recovered (R). There were three compartments for each of the E, I, and D states to more realistically capture distributions of duration of time spent in each state.

Fig. 2

Projected detected cases (A) and infections (B) at various levels of decarceration. We considered decarceration fractions of 0.0–0.9 by increments of 0.1. Decarcerated people were exposed to community level risks of cases and infections. We modeled two scenarios following decarceration including 1) no reduction in contacts and 2) reduced contacts proportional to the fraction decarcerated. ICE system status quo projected cases were consistent with those empirically observed (dashed line).

Fig. 3

Projected detected cases (A and C) and infections (B and D) at various levels of decarceration by origin of cases. Plots show how the total cases and infections are composed of those that occur in the community and ICE settings.