Evaluating the contributions of strategies to prevent SARS-CoV-2 transmission in the healthcare setting: a modelling study

  1. Xueting Qiu
  2. Joel C Miller
  3. Derek R MacFadden
  4. William P Hanage

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Abstract

Since its onset, the COVID-19 pandemic has caused significant morbidity and mortality worldwide, with particularly severe outcomes in healthcare institutions and congregate settings. To mitigate spread, healthcare systems have been cohorting patients to limit contacts between uninfected patients and potentially infected patients or healthcare workers (HCWs). A major challenge in managing the pandemic is the presence of currently asymptomatic/presymptomatic individuals capable of transmitting the virus, who could introduce COVID-19 into uninfected cohorts. The optimal combination of personal protective equipment (PPE), testing and other approaches to prevent these events is unclear, especially in light of ongoing limited resources.

Methods

Using stochastic simulations with a susceptible-exposed-infected-recovered dynamic model, we quantified and compared the impacts of PPE use, patient and HCWs surveillance testing and subcohorting strategies.

Results

In the base case without testing or PPE, the healthcare system was rapidly overwhelmed, and became a net contributor to the force of infection. We found that effective use of PPE by both HCWs and patients could prevent this scenario, while random testing of apparently asymptomatic/presymptomatic individuals on a weekly basis was less effective. We also found that even imperfect use of PPE could provide substantial protection by decreasing the force of infection. Importantly, we found that creating smaller patient/HCW-interaction subcohorts can provide additional resilience to outbreak development with limited resources.

Conclusion

These findings reinforce the importance of ensuring adequate PPE supplies even in the absence of testing and provide support for strict subcohorting regimens to reduce outbreak potential in healthcare institutions.

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  1. Version published to 10.1136/bmjopen-2020-044644
  2. ScreenIT

    SciScore for 10.1101/2020.04.20.20073080: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The simulations are scripted in Python (scripts can be found here – https://github.com/joelmiller/HospitalCOVID19).
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    We find that this makes little impact on the force of infection (with the caveat that it depends on the duration of the asymptomatic period), but it magnifies the impact of effective PPE. The potential for transmission from pre-symptomatic individuals has long been known to be a crucial component in how hard we expect it to be to control an infectious disease.22 This model confirms that if we wish to prevent SARS-CoV-2 transmission among the vulnerable noncovid cohort all individuals should be assumed to be infectious, both staff and patients. Where appropriate PPE is available it is being widely used throughout healthcare, and indeed use of cloth masks is now recommended for the general public by the Centers for Disease Control. However ample PPE may not continue to be available in all settings, and PPE for the non-COVID-19 cohort is an important element of planning. Notably, the impact of weekly random testing of staff and patients in the non-COVID cohort is unable to prevent infection becoming established in the absence of other interventions. These findings are also relevant to developing countries where testing may not be widely available, or anywhere a tradeoff exists between testing and PPE. Our findings regarding the size of sub cohorts are somewhat nuanced, but identifying some general trends is straightforward; reducing any of the transmission rates is unsurprisingly important in reducing transmission. However, by keeping subcohorts smaller, we reduce the probabilit...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2020.04.20.20073080 on medRxiv