A systematic review and meta-analysis of published research data on COVID-19 infection fatality rates

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Abstract

No abstract available

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  1. SciScore for 10.1101/2020.05.03.20089854: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    PubMed, MedLine, and Medrxiv were searched on the 25/04/2020 using the terms and Boolean operators: (infection fatality rate OR ifr OR seroprevalence) AND (COVID-19 OR SARS-CoV-2).
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    MedLine
    suggested: (MEDLINE, RRID:SCR_002185)
    GMK then conducted a simple Google and Google scholar search using the same terms to assess the grey literature, in particular published estimates from government agencies that may not appear on formal academic databases.
    Google
    suggested: (Google, RRID:SCR_017097)
    Google scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    Serological surveys were rated using a the risk of bias in prevalence tool with a resulting estimate in line with Cochrane GRADE criteria of low, moderate, or high (13).
    Cochrane GRADE
    suggested: None

    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 a number of limitations to this research. Importantly, the heterogeneity in the meta-analysis was very high. This may mean that the point-estimates are less reliable than would be expected. It is also notable that any meta-analysis is only as reliable as the data contained within – this research included a very broad range of studies that address slightly different questions with a very wide range of methodological rigor, and thus cannot represent certainty of any kind. While modelling studies were not formally graded, at least one has already been critiqued for simple mathematical errors, and given that many were pre-prints it is hard to ascertain if they have provided accurate representations of the data. Serology studies were at variable risk of bias, and analysing by only the highest quality serosurveys produced a higher estimate than relying on lower quality studies. Moreover, the quality of included serosurvey estimates was often questionable. Many countries have a clear political motivation to present lower estimates, making it challenging to ascertain whether these may have biased the reporting of results, particularly for those places that have only presented results as press releases thus far. Some have also been criticized for sampling issues that would likely lead to a biased overestimate of population infection rates (10). Accounting for right-censoring in these estimates was also a challenge. Using a 10-day cutoff for deaths is far too crude a method t...

    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. SciScore for 10.1101/2020.05.02.20088898: (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
    Laboratory Analyses: We assessed anti-SARS-CoV-2-IgG antibodies using a commercially available enzyme-linked immunosorbent assay (Euroimmun AG, Lübeck, Germany # EI 2606-9601 G) targeting the S1-domain of the spike protein of SARS-CoV-2; sera diluted 1:101 were processed on a EuroLabWorkstation ELISA.
    anti-SARS-CoV-2-IgG
    suggested: None

    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 also has some important limitations to acknowledge. First, the primary analyses include randomly selected participants as well as of members of their households, making this sample not entirely randomly selected. We attempt to adjust for some aspects of this through poststratification within our statistical model. Further, in sensitivity analyses we estimated the seroprevalence for just the original and found that it is similar to that of the full sample. Second, we were able to account for clustering within households only for overall prevalence pooling the three weeks, as there was not enough data to account for age, sex, and household clustering all together within this framework. The use of a random effects model to account for household clustering did not change pooled results. Finally, the recruitment of participants by email might exclude non tech-proficient individuals or people without access to technology; another survey specifically targeting vulnerable populations (socially and clinically) is ongoing. Over the next weeks, we will continue monitoring weekly seroprevalence in the general population and will be able to provide more refined analysis on symptomatology and other socio-demographic data in relation to immunological status. Yet, a preliminary presentation of these results is deemed to be necessary to timely inform global policy makers on how to adapt planning of the next phases. Public sharing of our protocol can also help the global academic com...

    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.