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  1. eLife

    Evaluation Summary:

    This paper by Dr. Abbas and colleagues uses genomic and epidemiological methods to track SARS-CoV-2 spread in a healthcare facility. It demonstrates that genomic data can be used to track the spread of viruses in healthcare environments and documents that inter-ward transmission is important in healthcare settings. Overall, the conclusions are supported by the data and analysis and the paper demonstrates that genomics may be an important adjunct tool for tracking the nosocomial transmission of respiratory viruses.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 and Reviewer #3 agreed to share their names with the authors.)

  2. eLife

    Reviewer #1 (Public Review):

    The authors combine genomic data, time of infection and ward sharing to identify nosocomial transmission events and reconstruct probable routes of transmission among patients and healthcare workers who work on either dedicated Covid-19 wards or on non-Covid-19 wards which nevertheless experienced an outbreak. Among their findings were that HCWs in Covid wards were less likely to transmit to their colleagues while those in outbreak wards were more likely to do, and patients with nosocomially acquired Covid-19 were more likely to be a source for other patients. They also find that inter-patient transmission was driven by ward-sharing but not necessarily room-sharing.

    Strengths and weaknesses:

    The work makes use of both epidemiological and genetic data, combining them using a robust Bayesian approach and using them to address meaningful questions. The basic methodology and dataset are identical to a previous study by the same authors ("Explosive nosocomial outbreak of SARS-CoV-2 in a rehabilitation clinic: the limits of genomics for outbreak reconstruction", Abbas et al. J Hosp Infect 2021). However, they address original relevant questions.

    Given what we know about close proximity as a risk factor for transmission, it is unfortunate that the intriguing result that room-sharing does not contribute much more to transmission than ward-sharing could not be examined in greater detail, taking into account the closeness of beds and actual patient interactions, such as through contact tracing. Similarly, analysis or discussion on the impact of sampling policies evolution over time and possible uncertainty regarding individual information such as dates of symptoms are lacking.

    The results are supported by the data and analysis performed, with appropriate levels of uncertainty.

    This is a very clear article that presents important results. Indeed, despite the very high level of SARS-CoV-2 nosocomial circulation in long-term care facilities over the past two years, studies investigating transmission routes and quantifying transmission strength are still very limited. This paper quantifies the relative risks arising from different hospital relationships which are relevant, particularly for medical professionals and those making decisions about infection control. Principal results of interest include that cases appearing in wards not dedicated to Covid-19 are more risky, and they add emphasis to the risk of an index case to all patients on the ward, not just the room. Furthermore, they demonstrate that genomic data, even in a relatively slowly evolving pathogen, can be utilised in gaining insight into hospital transmission, which will motivate more extensive collection and analysis of genomic data. It is unfortunate that repeating such an analysis would not be possible in most hospitals where sequencing viral genomes have not been possible or prioritised.

  3. eLife

    Reviewer #2 (Public Review):

    The authors performed epidemiological investigations, whole-genome sequencing (WGS) SARS-CoV-2 isolates, and sophisticated modeling to identify the likely SARS-CoV-2 transmission routes in a large geriatrics hospital. They found that HCW-to-HCW transmission in Covid wards was not higher than expected but the risk of transmission between HCWs in non-Covid wards was two-fold higher than expected. They also identified excess patient-to-patient transmission events, most of which occurred within the same ward, but not necessarily in the same room. Finally, they found that most transmission events were related to HCWs, but at least one was related to a patient with community-acquired Covid-19. This study provides information that is helpful to infection prevention programs that are trying to prevent the spread of SARS-CoV-2 within hospitals, especially in geriatric hospitals. The strength of the study is that the authors combined standard epidemiological investigations with sophisticated WGS and mathematical modeling. They also stress that WGS is not necessary to control spread but was important for assessing how SARS-CoV-2 was spread. The primary limitation was that the study was done at one hospital early in the pandemic so the results may not be generalizable to other hospitals later in the pandemic.

  4. eLife

    Reviewer #3 (Public Review):

    The paper by Abbas et al (2022) investigates the transmission chains of SARS-CoV-2 infections in four Swiss geriatric hospital wards that were affected by SARS-CoV-2 outbreaks. Using a Bayesian framework, the authors studied transmission patterns according to different types of cases (healthcare workers in COVID or non-COVID wards, patients with hospital-acquired infections) and the role of healthcare workers in the transmission process. The authors were able to reconstruct the transmission chains of the outbreak in the considered wards (with some uncertainty) and showed that HCWs experienced a higher risk of transmission in non-Covid wards in comparison with Covid wards.

    The strength of the study is that the authors combined epidemiological and genetic sequencing data from patients and HCWs to study the transmission chains in the affected hospital wards. Using a Bayesian modelling framework, they were able to reconstruct the transmission chains and present their estimates in form of distribution (instead of only single values) and therefore quantify uncertainty in their results.

    The coverage of their genetic sequencing was high, i.e., 82% of individuals who tested positive in the study were also sequenced. In addition, the authors had data on ward/room presence for patients and whether HCWs worked on COVID-wards or non-COVID wards. The authors used this information to substitute the missing contact data. The outbreaker2 package allowed for uncertainties in contact patterns by allowing non-infectious contacts to occur and incomplete reporting of contacts.

    Since the authors used specific data from a Swiss university-affiliated geriatric acute-care hospital from the first COVID-19 wave without detailed data on contact patterns or adherence to IPC measures, it remains unclear how their results and conclusions may generalize to other settings and future COVID-19 waves. While their results on the higher risk of within-ward transmission in non-COVID wards are interesting, their proposition on differential HCW behavior remains hypothetical without respective data.

    Abbas et al (2022) demonstrated how epidemiological and genetic sequence data can be combined and analyzed using the outbreaker2 package. Since they used the already existing open-source outbreaker2 package, their study is a good example for others to reconstruct outbreaks in other settings. In addition, it indicates where infection prevention strategies may need to focus and highlights the gaps in contact data information for healthcare settings.

  5. ScreenIT

    SciScore for 10.1101/2022.01.07.22268729: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Ethical considerations: The Ethics Committee of the Canton of Geneva (CCER), Switzerland, approved this study (CCER no. 2020-01330 and CCER no. 2020-00827).
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We performed SARS-CoV-2 whole genome sequencing (WGS) using an amplicon-based sequencing method in order to produce RNA sequences, as previously described [9] and summarised in the Supplement.
    WGS
    suggested: None
    Phylogenetic analysis: Sequence alignment was performed with MUSCLE (v3.8.31).
    MUSCLE
    suggested: (MUSCLE, RRID:SCR_011812)
    The evolutionary analyses were conducted in MEGA X [15] using the Maximum Likelihood method and Tamura 3-parameter model [16].
    MEGA
    suggested: (Mega BLAST, RRID:SCR_011920)

    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:
    Despite these strengths, some limitations must be acknowledged. First, we did not include sequences from CA-Covid cases, bar one. However the method we used to reconstruct who infected whom is able to cope with and identify missing intermediate cases; here we estimated that the overwhelming majority of cases (91.5%) was captured in our sample. Another limitation is that these investigations were performed during the first pandemic wave in a susceptible population, and therefore the results may no longer be applicable in settings with high vaccination coverage and/or substantial natural immunity. Nevertheless, the lessons learned may be useful in a large number of countries with slow vaccine roll-out due to vaccine hesitancy, particularly in HCWs where there is no vaccine mandate, or unequal access to vaccine supplies [30]. Furthermore, nosocomial outbreaks of SARS-CoV-2 still occur despite high vaccination coverage [31, 32]. Also, these valuable lessons may be applicable for nosocomial outbreak control in the case of future pandemics due to respiratory viruses with characteristics similar to SARS-CoV-2. In conclusion, strategies to prevent nosocomial SARS-CoV-2 transmission in geriatric settings should take into account the complex interplay between HCWs in dedicated Covid-19 wards versus non-Covid wards, and the potential for patient-to-patient transmission.

    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.

    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.

  6. Version 1 published on medRxiv