Serologic Surveillance and Phylogenetic Analysis of SARS-CoV-2 Infection Among Hospital Health Care Workers

  1. Jonne J. Sikkens
  2. David T. P. Buis
  3. Edgar J. G. Peters
  4. Mireille Dekker
  5. Michiel Schinkel
  6. Tom D. Y. Reijnders
  7. Alex. R. Schuurman
  8. Justin de Brabander
  9. A. H. Ayesha Lavell
  10. Jaap J. Maas
  11. Jelle Koopsen
  12. Alvin X. Han
  13. Colin A. Russell
  14. Janke Schinkel
  15. Marcel Jonges
  16. Sébastien Matamoros
  17. Suzanne Jurriaans
  18. Rosa van Mansfeld
  19. W. Joost Wiersinga
  20. Yvo M. Smulders
  21. Menno D. de Jong
  22. Marije K. Bomers

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Abstract

No abstract available

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  1. Version published to 10.1001/jamanetworkopen.2021.18554
  2. ScreenIT

    SciScore for 10.1101/2021.01.10.21249440: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study was approved by institutional review boards of both hospitals, and written informed consent was obtained from each participant.
    Consent: The study was approved by institutional review boards of both hospitals, and written informed consent was obtained from each participant.
    RandomizationConsensus full length SARS-CoV2 genomes (>29,000 nucleotide bases long with >100 minimum depth of coverage for each site) were generated by removing reads ends with PHRED scores <20 using Trimmomatic and mapping raw reads against the WIV04 reference genome (Genbank reference MN996528.1) using Bowtie 2.18–20 We used MAFFT (v7.427) to align SARS-CoV-2 sequences from HCW and patients, together with 300 randomly selected, contemporaneous SAR-CoV-2 virus genomes from the Netherlands (GISAID, Supplementary Data for accession numbers).
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    SARS-CoV-2 specific antibodies were measured in serum using the Wantai SARS-CoV-2 pan-Ig anti-S1-RBD test according to manufacturer’s instructions (Beijing Wantai ELISA, Bioscience Co. (Chongqing) CLIA, Zuhai Livzon ELISA).
    anti-S1-RBD
    suggested: None
    Software and Algorithms
    SentencesResources
    Consensus full length SARS-CoV2 genomes (>29,000 nucleotide bases long with >100 minimum depth of coverage for each site) were generated by removing reads ends with PHRED scores <20 using Trimmomatic and mapping raw reads against the WIV04 reference genome (Genbank reference MN996528.1) using Bowtie 2.18–20 We used MAFFT (v7.427) to align SARS-CoV-2 sequences from HCW and patients, together with 300 randomly selected, contemporaneous SAR-CoV-2 virus genomes from the Netherlands (GISAID, Supplementary Data for accession numbers).
    Trimmomatic
    suggested: (Trimmomatic, RRID:SCR_011848)
    Bowtie
    suggested: (Bowtie, RRID:SCR_005476)
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    21 We inferred a maximum likelihood tree with IQ-TREE (v2.0.6) using the HKY+I+G model.
    IQ-TREE
    suggested: (IQ-TREE, RRID:SCR_017254)
    23 We used BEAST (v1.10.4) to reconstruct a Bayesian time-scaled phylogenetic tree for the same set of sequences using the HKY+I+G model with a strict molecular clock, exponential growth prior, and an informative clock prior based on recent estimates of SARS-CoV-2 substitution rate (G-distribution prior with a mean of 0.8 × 10–3 subs/site/year and standard deviation of 5 × 10–4.24,25 We performed and combined two chains of 100 million steps.
    BEAST
    suggested: (BEAST, RRID:SCR_010228)

    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:
    Our study has a number of limitations. First, despite the prospective cohort design, selection bias cannot be completely ruled out, e.g. HCW staying at home ill were not able to enroll if this happened during the first measurement resulting in underestimating of incidence. Second, not all nasopharyngeal samples from patients and HCW collected for SARS-CoV-2 NAAT were available for viral sequencing analyses as they were either not stored or the admitted patients were diagnosed elsewhere. As such, there could be missing clusters and/or missing links in the transmission clusters that were inferred. Third, no systematic data on compliance to infection prevention measures were collected, limiting more precise conclusions. Fourth, infection incidence was substantially higher on one specific Covid-19 ward, which also contributed the majority of transmission clusters. However, when excluding this ward, the proportion of seroconverted HCW on regular Covid-19 wards remained more than double as high when compared to intensive care, emergency room or non-covid-19 wards. Preventing SARS-CoV-2 infection in HCW is important for the health of the individual HCW, to halt the ongoing pandemic and to maintain a functioning healthcare system. Understandably, much attention has been focused on preventing patient-to-HCW transmission. Our results show that working in hospital patient care leaves HCW vulnerable to infection through HCW-to-HCW transmission, which has received less attention and deser...

    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

    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.

  3. Version published to 10.1101/2021.01.10.21249440v1 on medRxiv