The impact of testing and infection prevention and control strategies on within-hospital transmission dynamics of COVID-19 in English hospitals

  1. Stephanie Evans
  2. Emily Agnew
  3. Emilia Vynnycky
  4. James Stimson
  5. Alex Bhattacharya
  6. Christopher Rooney
  7. Ben Warne
  8. Julie Robotham

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Abstract

Nosocomial transmission of SARS-CoV-2 is a key concern, and evaluating the effect of testing and infection prevention and control strategies is essential for guiding policy in this area. Using a within-hospital SEIR transition model of SARS-CoV-2 in a typical English hospital, we estimate that between 9 March 2020 and 17 July 2020 approximately 20% of infections in inpatients, and 73% of infections in healthcare workers (HCWs) were due to nosocomial transmission. Model results suggest that placing suspected COVID-19 patients in single rooms or bays has the potential to reduce hospital-acquired infections in patients by up to 35%. Periodic testing of HCWs has a smaller effect on the number of hospital-acquired COVID-19 cases in patients, but reduces infection in HCWs by as much as 37% and results in only a small proportion of staff absences (approx. 0.3% per day). This is considerably less than the 20–25% of staff that have been reported to be absent from work owing to suspected COVID-19 and self-isolation. Model-based evaluations of interventions, informed by data collected so far, can help to inform policy as the pandemic progresses and help prevent transmission in the vulnerable hospital population.

This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.

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  1. Version published to 10.1098/rstb.2020.0268
  2. ScreenIT

    SciScore for 10.1101/2020.05.12.20095562: (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
    Partial-rank correlation coefficients were used to calculate the most significant parameter values using the spartan R package (Alden et al., 2013).
    spartan
    suggested: (SPARTAN, RRID:SCR_014901)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    A limitation of this work is that this model does not separate asymptomatic from pre-symptomatic. Further, the model is deterministic and cannot account for the impact of outbreaks on individual wards, or the sequestering of patients and HCWs onto “hot” and “cold” wards. Despite these limitations, the results from this work have the potential to impact infection control during coronavirus outbreaks.

    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.

    About SciScore

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  3. Version published to 10.1101/2020.05.12.20095562v2 on medRxiv
  4. Version published to 10.1101/2020.05.12.20095562v1 on medRxiv