How well does SARS-CoV-2 spread in hospitals?

  1. George Shirreff
  2. Jean-Ralph Zahar
  3. Simon Cauchemez
  4. Laura Temime
  5. Lulla Opatowski
  6. EMEA-MESuRS working group on the nosocomial modelling of SARS-CoV-2

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Covid-19 poses significant risk of nosocomial transmission, and preventing this requires good estimates of the basic reproduction number R 0 in hospitals and care facilities, but these are currently lacking. Such estimates are challenging due to small population sizes in these facilities and inconsistent testing practices.

We estimate the patient-to-patient R 0 and daily transmission rate of SARS-CoV-2 using data from a closely monitored hospital outbreak in Paris 2020 during the first wave. We use a realistic epidemic model which accounts for progressive stages of infection, stochastic effects and a large proportion of asymptomatic infections. Innovatively, we explicitly include changes in testing capacity over time, as well as the evolving sensitivity of PCR testing at different stages of infection. We conduct rigorous statistical inference using iterative particle filtering to fit the model to the observed patient data and validate this methodology using simulation.

We provide estimates for R 0 across the entire hospital (2.6) and in individual wards (from 3 to 15), possibly reflecting heterogeneity in contact patterns or control measures. An obligatory mask-wearing policy introduced during the outbreak is likely to have changed the R 0 , and we estimate values before (8.7) and after (1.3) its introduction, corresponding to a policy efficacy of 85%.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.09.28.21264066: (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

    No key resources detected.

    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:
    There are several limitations to the analysis as a result of simplifying assumptions. Firstly, when running the entire hospital analysis, the ward structure was not accounted for, even though it could be assumed that the rate of intra-ward transmission is higher than inter-ward. Secondly, we did not take into account the possibility of imported infections other than from the index case(s), preferring to assume that after the very early stages of the outbreak, the force of infection from other patients would substantially outweigh those arriving from the community. Thirdly, we focused on patients and did not explicitly model acquisition nor transmission by or from HCWs. No data on infection status for the health care workers within the hospital over the study period was available. Health care workers were therefore indirectly modelled as vectors of transmission through the patient-patient infection process. Rates of transmission to HCWs exposed to infectious patients are relatively low [35,36], as well as from HCWs to patients [37], although this may have been less true in the early stages of the pandemic, given low levels of hand hygiene [38]. Ignoring the contribution of HCWs to new infections in the analysis suggests that we may have overestimated the transmission risk from infectious patients. However, our estimates can still be interpreted as valid measures of the nosocomial risk to patients. Fourthly, we note that the decision to analyze data from this hospital is partly...

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.09.28.21264066v1 on medRxiv