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Review 2: "Comparison of infection control strategies to reduce COVID-19 outbreaks in homeless shelters in the United States: a simulation study"

Reviewers find this study a generally reliable and important contribution to understanding infection control strategies in a high-risk setting, though several assumptions in the model could be clarified.

Published onNov 06, 2020
Review 2: "Comparison of infection control strategies to reduce COVID-19 outbreaks in homeless shelters in the United States: a simulation study"
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key-enterThis Pub is a Review of
Comparison of infection control strategies to reduce COVID-19 outbreaks in homeless shelters in the United States: a simulation study

Background: Multiple COVID-19 outbreaks have occurred in homeless shelters across the US, highlighting an urgent need to identify the most effective infection control strategy to prevent future outbreaks. Methods: We developed a microsimulation model of SARS-CoV-2 transmission in a homeless shelter and calibrated it to data from cross-sectional polymerase-chain-reaction (PCR) surveys conducted during COVID-19 outbreaks in five shelters in three US cities from March 28 to April 10, 2020. We estimated the probability of averting a COVID-19 outbreak in a representative homeless shelter of 250 residents and 50 staff over 30 days under different infection control strategies, including daily symptom-based screening, twice-weekly PCR testing and universal mask wearing. Results: The proportion of PCR-positive residents and staff at the shelters with observed outbreaks ranged from 2.6% to 51.6%, which translated to basic reproduction number (R0) estimates of 2.9-6.2. The probability of averting an outbreak diminished with higher transmissibility (R0) within the simulated shelter and increasing incidence in the local community. With moderate community incidence (~30 confirmed cases/1,000,000 people/day), the estimated probabilities of averting an outbreak in a low-risk (R0=1.5), moderate-risk (R0=2.9), and high-risk (R0=6.2) shelter were, respectively: 0.33, 0.11 and 0.03 for daily symptom-based screening; 0.52, 0.27, and 0.04 for twice-weekly PCR testing; 0.47, 0.20 and 0.06 for universal masking; and 0.68, 0.40 and 0.08 for these strategies combined. Conclusions: In high-risk homeless shelter environments and locations with high community incidence of COVID-19, even intensive infection control strategies (incorporating daily symptom-screening, frequent PCR testing and universal mask wearing) are unlikely to prevent outbreaks, suggesting a need for non-congregate housing arrangements for people experiencing homelessness. In lower-risk environments, combined interventions should be adopted to reduce outbreak risk.

RR:C19 Evidence Scale rating by reviewer:

  • Reliable. The main study claims are generally justified by its methods and data. The results and conclusions are likely to be similar to the hypothetical ideal study. There are some minor caveats or limitations, but they would/do not change the major claims of the study. The study provides sufficient strength of evidence on its own that its main claims should be considered actionable, with some room for future revision.



The preprint of “Comparison of infection control strategies to reduce COVID-19 outbreaks in homeless shelters in the United States: a simulation study” presents a microsimulation model of COVID-19 transmission in a representative homeless shelter over 30 days under simulated combinations of infection control strategies. Severe acute respiratory syndrome coronavirus 2, the novel coronavirus that causes COVID-19, has had a disproportionate impact on people living in congregate settings, including nursing homes, correctional facilities, and homeless shelters. As noted in the preprint, this analysis is an important contribution to understanding which infection control strategies are the most effective in preventing COVID-19 transmission and minimizing outbreaks in homeless shelters—a population that has been historically underserved—to protect residents and staff.

The manuscript could be strengthened by addressing the following points:

First, the one-day turnaround time for PCR test results used in the model parameters is not realistic; most state and private laboratories have required more time (up to several days) for processing samples and resulting test-takers. Staff and residents would need to be isolated for more than one day while awaiting results. The model should account for a longer turnaround time for PCR test results, ideally informed by the current turnaround times for the state public health laboratories in the three selected settings (Boston, Seattle, and San Francisco).

Second, the preprint states that the initial population used in the modeling has the same composition of risk factors (age and co-morbidities) as the San Francisco shelter population. Since age and demographic characteristics determine whether an individual is treated as “low-risk” or “high-risk” in the model, a description of the demographic characteristics of the model population should be included in the manuscript. For example, in Supplementary Table 2, the probability of developing clinical symptoms for “low risk (age < 60 years + no co-morbidities)” and “moderate risk (age < 60 years + co-morbidities)” is the same (0.473), but individuals of any age with certain underlying medical conditions may have different time to presentation of fever and respiratory symptoms.

Third, individuals who have recovered from COVID-19 are assumed to remain immune in the microsimulation, but emerging research suggests that immunity may be time-limited. The possibility of reinfection is still not well-understood and so this point should be acknowledged in the description of the parameters.

Finally, the manuscript does not consider contact tracing to identify and quarantine exposed individuals within the model population, which would increase the number of individuals in isolation and also contribute to averting an outbreak.

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