Because ICE has shared little information with the public beyond demonstrating that it has tested few people passing through its custody, Vera simulated many outcomes for a disease outbreak that could have occurred within a 60-day period.^[]Researchers typically run simulations numerous times to produce a range of outcomes and then summarize the results to show the center of the distribution of outcomes. On each day in the 60-day period of the Vera simulation, each detained person’s probability for disease progression was independent of their probability for transfer or book out, with the exception of people who were hospitalized on that day or had died and were restricted from being transferred or booked-out in the simulation. Depending on each person’s varied outcomes, repeated simulations yielded different results. To summarize patterns between repeated simulations, the results presented here are the median results of 500 simulations (unless otherwise noted).

This model assumes approximately 751 people booked in by ICE per day and 80,655 people cumulatively held in ICE detention at any point during the 60-day period. Due to limitations in the data ICE shares with the public, Vera could not use the true number of people ICE detained during this time in its model. Instead, the simulation shows a relationship between a given population and the number of people within that population who would be infected.^[]The results of Vera’s simulation indicate the order of magnitude of the spread of COVID-19 among people who ICE has detained. The actual simulation numbers should not be interpreted as precise predictions for several reasons. First, given the absence of empirical studies of the spread of COVID-19 in detained or incarceration settings, Vera used educated guesses to select model parameters based on available information and choices made in other COVID-19 papers. Second, because of the unavailability of current ICE data, the model uses historical data to model the system of ICE detention. Vera’s simulation is only provisionally calibrated to the reported March 2020 trends in the number of people that ICE currently detains or books-in to detention. Third, ICE’s lack of testing and the resulting uncertainty about when the first people in each facility may have been infected led, in large part, to the non-trivial variability represented by the wide prediction intervals generated by the simulation. To summarize, ICE’s lack of transparency makes it hard to build an accurate model.

The model predicts that on day 60, 15 percent of people within the currently detained population would have active COVID-19 cases (excluding those who have recovered from the virus), as shown in Figure 2. At the time of day 60, the outbreak would not yet have peaked.

Cumulatively, the model predicts that by the end of the 60-day period:

• An estimated 15,549 people would contract COVID-19—which is 19 percent of people who were in ICE detention at any point during the simulation period.^[]The fraction of the currently detained population with active COVID-19 cases as of day 60 can be less than the fraction of the cumulative detained population that was ever infected. The first contributor to this difference is the number of people who have recovered from COVID-19 prior to day 60 and remain detained; they are counted in the numerator for cumulative infections but not current infections on day 60. The second contributor to this difference is that by day 60, people whom ICE books out of detention are replaced by people booked in who, due to a relatively shorter detention duration, are less likely to be infected, thus lowering the currently infected fraction of people in detention on day 60. (See Figure 3.)
• An estimated 253 people would have COVID-19 cases that progress to the point of requiring serious medical attention such as hospitalization, presumably the kind of care that would not be possible from inside detention. (See Figure 4.) By day 60, at which point infections would not yet have peaked, an estimated 2 to 17 people would die in ICE custody from COVID-19.^[]This range of 2 to 17 people represents the 95 percent prediction interval for cumulative deaths in ICE custody from COVID-19 by day 60.

Vera’s model does not project disease progression for people who become infected with COVID-19 while in custody and who are subsequently booked out as a result of release or deportation, so it is likely that the number of people who would require serious medical intervention or who would die within the 60-day period may be even higher. Similarly, the model does not track disease progression for people who remain detained past day 60 of the simulation, which may result in an underestimate of the harm inflicted on detained people over a longer time frame.

Vera’s predictions differ drastically from the confirmed cases ICE has reported to date. Since ICE reported the first COVID-19 case for a person in detention on March 24, 2020, ICE has regularly updated the total number of confirmed COVID-19 cases on its website. Vera worked backward from the date of the first ICE-confirmed COVID-19 case to align the 60-day simulation period with the time frame of ICE-reported cases, setting March 17 as day one of the simulation (allowing for a seven-day expected lag between exposure to the virus and the onset of symptoms, as assumed in Vera’s model). Figure 3 illustrates the cumulative number of people with COVID-19 (predicted and ICE-reported). As of day 60 in the simulation, which corresponds to May 15, 2020, the model predicts the number of confirmed COVID-19 cases would be 15 times higher than the number of confirmed cases ICE had reported by that date (15,549 cases predicted by the model versus 986 cases reported by ICE). The number of predicted cases far surpasses the number of COVID-19 tests ICE has reportedly administered to date. Indeed, the model predicts that by May 15, 2020, the number of COVID-19 cases among people in detention would be eight times higher than the number of tests ICE had even administered.

Vera’s model suggests that transfers pose additional, avoidable risk to detained people during the pandemic. Even before the pandemic, ICE did not publicly disclose the extent to which it transferred people across its network of detention facilities. During the simulation period, much of the country was living under stay-at-home orders, and domestic and international travel as well as most border entries were significantly reduced in an effort to control the spread of COVID-19.^[] Michael D. Shear and Zolan Kanno-Youngs, “Trump Administration Plans to Extend Virus Border Restrictions Indefinitely,” May 13, 2020, The New York Times, https://perma.cc/J369-44WR. Despite this, ICE continues to conceal its transfer activity from the public. If ICE continued to follow transfer patterns of fiscal year 2016, the last year it released data publicly on its transfers, the model shows that over the course of 500 simulations, ICE would conduct a median cumulative 1,744 transfers of people with active COVID-19 cases to other detention facilities by the end of the 60-day period. (See Figure 5.) This includes repeat transfers of the same people and transfers of people to facilities across state lines and to regions across the country.^[]Some unknown number of detention facilities in use during fiscal year 2016 may no longer be in use today. ICE has reportedly detained people at 29 additional facilities as of April 2020, which were not found in its fiscal year 2016 detention data, including several newer facilities in Texas (Montgomery ICE Processing Center, El Valle Detention Facility, Prairieland Detention Facility, Limestone County Detention Center, Webb County Detention Center, Coastal Bend Detention Facility, Dallas County Jail) and Louisiana (Winn Correctional Center, Jackson Parish Correctional Center, Richwood Correctional Center, Catahoula Correctional Center, LaSalle Correctional Center Olla, River Correctional Center). By day 60 of the simulation, 9 percent of people who ever had COVID-19 would be transferred at least once by ICE while actively contagious. Each frame in Figure 6 shows the cumulative transfers of people with COVID-19 by the end of a single run of the simulation. As federal and state governments grapple with the management and consequences of the pandemic, Vera’s analysis demonstrates that ICE transfers pose a substantial risk of transmission across the United States.