Infection fatality rate of COVID-19 inferred from seroprevalence data

  1. John P A Ioannidis

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.05.13.20101253: (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
    Searches were made in PubMed (LitCOVID), medRxiv, bioRxiv, and Research Square using the terms “seroprevalence” and “antibodies” with continuous updates (last update July 11, 2020).
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    bioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)

    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:
    Both undercounting and overcounting of COVID-19 deaths may be a caveat in different locations and this is difficult to settle in the absence of very careful scrutiny of medical records and autopsies. The Tokyo data,29 nevertheless, also show similarly very low IFR. Moreover, evaluation of all-cause mortality in Japan has shown no excess deaths during the pandemic, consistent with the possibility that somehow the Japanese population was spared. Very low IFRs seem common in Asian countries, including China (excluding Wuhan), Iran, Israel and India. Former immunity from exposure to other coronaviruses, genetic differences, hygienic etiquette, lower infectious load, and other unknown factors may be speculated. IFR seems to be very low also in Singapore where extensive PCR testing was carried out. As of July 12, 2020, in Singapore there were only 26 deaths among 46,283 cases, suggesting an upper bound of 0.06% for IFR, even if no cases had been missed. Some surveys have also been designed to assess seroprevalence repeatedly spacing out measurements in the same population over time. A typical pattern that seems to emerge is that seroprevalence may increase several fold within a few weeks, but plateau or even decline may follow.10,28 A more prominent decline of seropositivity was seen in a study in Wuhan.32 Genuine decrease may be difficult to differentiate from random variation. However, some preliminary data60,61 suggest that decrease in antibody titers may be fast. Decrease in se...

    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.

  2. Rapid Reviews Infectious Diseases

    Timothy Hallett

    Review 1: "The infection fatality rate of COVID-19 inferred from seroprevalence data"

    This study finds substantial heterogeneity in the infection fatality rate across different locations. Data are useful and add to the emerging picture on IFR, however substantial conclusions cannot be drawn.

  3. Rapid Reviews Infectious Diseases

    Kenji Mizumoto, Gerardo Chowell

    Review 2: "The infection fatality rate of COVID-19 inferred from seroprevalence data"

    This study finds substantial heterogeneity in the infection fatality rate across different locations. Data are useful and add to the emerging picture on IFR, however substantial conclusions cannot be drawn.

  4. Rapid Reviews Infectious Diseases

    Abraham D. Flaxman

    Review 3: "The infection fatality rate of COVID-19 inferred from seroprevalence data"

    This study finds substantial heterogeneity in the infection fatality rate across different locations. Data are useful and add to the emerging picture on IFR, however, substantial conclusions cannot be drawn.

  5. Rapid Reviews Infectious Diseases

    Strength of evidence

    Reviewers: Timothy Hallett (Imperial College London) | 📒📒📒 ◻️◻️
    Kenji Mizumoto (Kyoto University), Gerardo Chowell (Georgia State University) | 📒📒📒 ◻️ ◻️
    Abraham D. Flaxman (University of Washington) | 📕 ◻️◻️◻️◻️

  6. Version published to 10.2471/blt.20.265892
  7. Version published to 10.1101/2020.05.13.20101253v3 on medRxiv
  8. ScreenIT

    SciScore for 10.1101/2020.05.07.20093963: (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

    Antibodies
    SentencesResources
    The potential effect of the viral inactivation protocol on the measurement of antibody levels was assessed using serum positive for anti-malaria antibodies.
    anti-malaria
    suggested: None
    IgG and IgM antibody levels were measured in matched samples before and after the inactivation protocol.
    IgM
    suggested: None
    For IgM measurements, serum samples were diluted 1/200, and R-Phycoerythrin (R-PE) - conjugated Donkey Anti-Human IgM (cat#709-116-073; JacksonImmunoResearch, UK) antibody was used as secondary antibody at 1/400 dilution.
    Anti-Human IgM
    suggested: (Jackson ImmunoResearch Labs Cat# 709-116-073, RRID:AB_2340518)
    For IgG, serum samples were diluted 1/100, and R-Phycoerythrin (R-PE) -conjugated Donkey Anti-Human IgG (cat#709-116-098; JacksonImmunoResearch, UK) antibody was used as secondary antibody at 1/120 dilution.
    R-Phycoerythrin
    suggested: (Jackson ImmunoResearch Labs Cat# 709-116-098, RRID:AB_2340519)
    Anti-Human IgG
    suggested: (Jackson ImmunoResearch Labs Cat# 709-116-098, RRID:AB_2340519)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Modelling beyond the timeframe for which we have data has its limitations, however our approach benefits from robust quantification of uncertainty accounting for a wide range of future scenarios. Furthermore, this modelling approach provides falsifiable predictions which will allow models to be updated as our team and others generate new data. For the purpose of evaluation of antibody kinetics, measured antibody responses from samples collected from individuals followed longitudinally after confirmed SARS-CoV-2 infection will be especially valuable. The simulations presented here predict that following SARS-CoV-2 infection, antibody responses will increase rapidly 1-2 weeks after symptom onset, with antibody responses peaking within 2-4 weeks. After this peak, antibody responses are predicted to decline according to a bi-phasic pattern, with rapid decay in the first three to six months followed by a slower rate of decay. Model predictions of the rise and peak of antibody response are informed by, and are consistent with, many sources of data [10-14,36]. Model predictions of the decay of antibody responses are strongly determined by prior information on longitudinal follow-up of individuals infected with other coronaviruses [26-31]. Under the scenario that the decay of SARS-CoV-2 antibody responses is similar to that of SARS-CoV, we would expect substantial reductions in antibody levels within the first year after infection. For the seropositivity cutoffs highlighted here, thi...

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04262921RecruitingFrench COVID Cohort

    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.

  9. Version published to 10.1101/2020.05.13.20101253v2 on medRxiv
  10. Version published to 10.1101/2020.05.13.20101253v1 on medRxiv