One-year surveillance of SARS-CoV-2 transmission of the ELISA cohort: A model for population-based monitoring of infection risk

  1. Christine Klein
  2. Max Borsche
  3. Alexander Balck
  4. Bandik Föh
  5. Johann Rahmöller
  6. Elke Peters
  7. Jan Knickmann
  8. Miranda Lane
  9. Eva-Juliane Vollstedt
  10. Susanne A. Elsner
  11. Nadja Käding
  12. Susanne Hauswaldt
  13. Tanja Lange
  14. Jennifer E. Hundt
  15. Selina Lehrian
  16. Julia Giese
  17. Alexander Mischnik
  18. Stefan Niemann
  19. Florian Maurer
  20. Susanne Homolka
  21. Laura Paulowski
  22. Jan Kramer
  23. Christoph Twesten
  24. Christian Sina
  25. Gabriele Gillessen-Kaesbach
  26. Hauke Busch
  27. Marc Ehlers
  28. Stefan Taube
  29. Jan Rupp
  30. Alexander Katalinic

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Abstract

With newly rising coronavirus disease 2019 (COVID-19) cases, important data gaps remain on (i) long-term dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection rates in fixed cohorts (ii) identification of risk factors, and (iii) establishment of effective surveillance strategies. By polymerase chain reaction and antibody testing of 1% of the local population and >90,000 app-based datasets, the present study surveilled a catchment area of 300,000 inhabitants from March 2020 to February 2021. Cohort (56% female; mean age, 45.6 years) retention was 75 to 98%. Increased risk for seropositivity was detected in several high-exposure groups, especially nurses. Unreported infections dropped from 92 to 29% during the study. “Contact to COVID-19–affected” was the strongest risk factor, whereas public transportation, having children in school, or tourism did not affect infection rates. With the first SARS-CoV-2 cohort study, we provide a transferable model for effective surveillance, enabling monitoring of reinfection rates and increased preparedness for future pandemics.

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  1. Version published to 10.1126/sciadv.abm5016
  2. ScreenIT

    SciScore for 10.1101/2021.05.10.21256966: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Study oversight and design: The ELISA (Lübeck Longitudinal Investigation of SARS-CoV-2 Infection) study was approved by the ethics committee of the University of Lübeck (Az. 20-150) and sponsored by the Federal Ministry for Education and Research (BMBF), the State of Schleswig-Holstein, the University of Lübeck, and by a crowd-funding campaign organized by the University of Lübeck.
    Sex as a biological variablenot detected.
    Randomization, 2145 individuals matched by age and sex distribution of the study region were randomly drawn to comprise a population-based group.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line AuthenticationAuthentication: , Lübeck, using validated PCR platforms.

    Table 2: Resources

    Antibodies
    SentencesResources
    All participants were tested for current or previous SARS-CoV-2 infection using nucleic-acid and anti-spike protein (extracellular S1 part) IgG antibody testing with support of a mobile app-based questionnaire, electronic scheduling and result reporting system.
    anti-spike protein ( extracellular S1 part ) IgG
    suggested: None
    The EUROIMMUN SARS-CoV-2 S1 IgG (#EI 2606-9601-2 G) ELISA was performed according to the manufacturer’s instructions for examination time-points 1, 6, and 7 and partially for time-points 3 and 5 for participants with increased anti-S1 IgG antibody titers at time-point 1.
    anti-S1 IgG
    suggested: None
    Participants with an antibody titer against the S1 antigen of >1.1 (compared to an anti-S1 IgG-positive reference serum provided by the manufacturer in the ELISA detection system) were defined as anti-S1 IgG antibody-positive.
    anti-S1 IgG-positive
    suggested: None
    anti-S1 IgG antibody-positive.
    suggested: None
    Software and Algorithms
    SentencesResources
    These numbers were supported by mobility data from Google, which noted a significant increase in the use of public transportation relative to all of Germany (p-value <10^-30, moderated F-test) including other German touristic hotspots, particularly Bavaria (Figure 2.
    Google
    suggested: (Google, RRID:SCR_017097)

    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:
    Limitations are the relatively wide-spread examination time-points in the fall/winter 2020/2021 and overrepresentation of highly motivated study participants with a slightly above-average education level. In conclusion, we i) provide a model for effective, regional surveillance of the pandemic; ii) underscore the role of vaccination especially in low-prevalence regions remaining far from herd immunity; iii) identify plausible infection risk factors informing public health measures; iv) demonstrate that easing of lockdown measures appears safe even over several months at times of very low prevalence rates; v) underline that continuous monitoring of infections and immediate and consequent imposition of appropriate measures are mandatory to contain virus spread in case of an incipient rise in infection rates.

    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.

  3. Version published to 10.1101/2021.05.10.21256966 on medRxiv