A note on COVID-19 seroprevalence studies: a meta-analysis using hierarchical modelling
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Abstract
In recent weeks, several seroprevalence studies have appeared which attempt to determine the prevalence of antibodies against SARS-CoV-2 in the population of certain European and American locations. Many of these studies find an antibody prevalence comparable to the false positive rate of their respective serology tests and the relatively low statistical power associated with each study has invited criticism. To determine the strength of the signal, we perform a meta-analysis on the publicly available seroprevalence data based on Bayesian hierarchical modelling with Markov Chain Monte Carlo and Generalized Linear Mixed Modelling with prediction sampling. We examine studies with results from Santa Clara County (CA), Los Angeles County (CA), San Miguel County (CO), Chelsea (MA), the Comté de Genève (Switzerland), and Gangelt (Germany). Our results are in broad agreement with the conclusions of the studies; we find that there is evidence for non-trivial levels of antibody prevalence across all study locations. However, we also find that a significant probability mass exists for antibody prevalence at levels lower than the reported figures. The results of our meta-analysis on the recent seroprevalence studies point to an important and strongly suggestive signal.
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SciScore for 10.1101/2020.05.03.20089201: (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
No key resources detected.
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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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…
SciScore for 10.1101/2020.05.03.20089201: (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
No key resources detected.
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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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.
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