Quantifying antibody kinetics and RNA detection during early-phase SARS-CoV-2 infection by time since symptom onset
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Abstract
Understanding and mitigating SARS-CoV-2 transmission hinges on antibody and viral RNA data that inform exposure and shedding, but extensive variation in assays, study group demographics and laboratory protocols across published studies confounds inference of true biological patterns. Our meta-analysis leverages 3214 datapoints from 516 individuals in 21 studies to reveal that seroconversion of both IgG and IgM occurs around 12 days post-symptom onset (range 1–40), with extensive individual variation that is not significantly associated with disease severity. IgG and IgM detection probabilities increase from roughly 10% at symptom onset to 98–100% by day 22, after which IgM wanes while IgG remains reliably detectable. RNA detection probability decreases from roughly 90% to zero by day 30, and is highest in feces and lower respiratory tract samples. Our findings provide a coherent evidence base for interpreting clinical diagnostics, and for the mathematical models and serological surveys that underpin public health policies.
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SciScore for 10.1101/2020.05.15.20103275: (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 Sentences Resources Estimating the distribution of seroconversion times: One of the goals of this study is to estimate the means and variation of IgG and IgM seroconversion times (time between symptom onset and first antibody detection) for different assays, antigens, and disease severity. antigens ,suggested: NoneWhen reporting ELISA results in the main text, IgG results are shown for assays using NP as target antigen (ELISA-NP), and IgM results are shown for assays using the Spike antigen (ELISA-Spike), as these assays are most often used for the two antibody types (Sethuraman et al. 2020; To et al. 2020). NP as target …SciScore for 10.1101/2020.05.15.20103275: (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 Sentences Resources Estimating the distribution of seroconversion times: One of the goals of this study is to estimate the means and variation of IgG and IgM seroconversion times (time between symptom onset and first antibody detection) for different assays, antigens, and disease severity. antigens ,suggested: NoneWhen reporting ELISA results in the main text, IgG results are shown for assays using NP as target antigen (ELISA-NP), and IgM results are shown for assays using the Spike antigen (ELISA-Spike), as these assays are most often used for the two antibody types (Sethuraman et al. 2020; To et al. 2020). NP as target antigen (ELISA-NP),suggested: NoneSoftware and Algorithms Sentences Resources Different combinations of search terms were used in order to maximize the likelihood of finding an article through Pubmed Pubmedsuggested: (PubMed, RRID:SCR_004846), Google Scholar, and medRxiv. Google Scholarsuggested: (Google Scholar, RRID:SCR_008878)Data were extracted from published material, and digitized from figures when necessary using WebPlotDigitizer (Rohatgi 2019). WebPlotDigitizersuggested: (WebPlotDigitizer, RRID:SCR_013996)All data preparation, cleaning, analysis and plotting was done in R version 3.6.1 (R Core Team 2019) using packages ggplot2 (Wickham 2016), dplyr (Wickham et al. 2019), readxl (Wickham & Bryan 2019) ggplot2suggested: (ggplot2, RRID:SCR_014601)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:One potential caveat related to any type of result reported as time since symptom onset is that variation in the incubation period (time between infection and symptom onset) can affect the estimated timing of antibody kinetics and RNA shedding. The mean incubation period is estimated to be around 7–8 days, with a standard deviation of 4.4 (Ma et al. 2020). The clear antibody and RNA detection patterns we observe here suggest that the effect of this variation does not obscure broad patterns, but if incubation period does differ in certain groups of individuals, relative results may be affected. This could indeed be the case for disease severity, as mild cases are estimated to have a longer incubation period (8.3 days) than severe cases (6.5 days) (Ma et al. 2020). Patterns of IgM and IgG detection align with immunological expectations, as IgM antibodies are typically present during the early phase of the immune response while IgG antibodies remain detectable for much longer periods (Xiao et al. 2020). We detected IgG and IgM antibodies in nearly all (98–100%) individuals by day 22–23 after symptom onset, consistent with recent findings (Kraay et al. 2020). While IgG detection remains at this level for at least the remainder of available times in the dataset (60 days), the proportion of IgM-positive samples decreases again, reaching 60–70% by 60 dpo. In other words, the proportion of individuals losing IgM increases from day 30 onwards. The quantification of changes in detectio...
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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SciScore for 10.1101/2020.05.15.20103275: (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 Sentences Resources This formal integration approach enabled us to leverage 3,214 data points from 516 individuals with symptoms ranging from asymptomatic to critical, published in 22 studies, resulting in a quantitative synthesis of diverse data on antiSARS-CoV-2 antibody patterns and RNA shedding during the early phase of infection. antiSARS-CoV-2suggested: NoneMethods Article selection We considered preprints and peer-reviewed articles reporting the presence (positive or negative) or levels for IgG, IgM or neutralizing antibodies against SARS-CoV-2 … SciScore for 10.1101/2020.05.15.20103275: (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 Sentences Resources This formal integration approach enabled us to leverage 3,214 data points from 516 individuals with symptoms ranging from asymptomatic to critical, published in 22 studies, resulting in a quantitative synthesis of diverse data on antiSARS-CoV-2 antibody patterns and RNA shedding during the early phase of infection. antiSARS-CoV-2suggested: NoneMethods Article selection We considered preprints and peer-reviewed articles reporting the presence (positive or negative) or levels for IgG, IgM or neutralizing antibodies against SARS-CoV-2 or SARSrelated CoV RaTG13 measured by enzyme-linked immunosorbent assay ( SARS-CoV-2suggested: (Sino Biological Cat# 40143-R019, AB_2827973)Estimating the distribution of seroconversion times One of the goals of this study is to estimate the means and variation of IgG and IgM seroconversion times ( time between symptom onset and first antibody detection ) for different assays , antigens , and disease severity . antigens ,suggested: NoneWhen reporting ELISA results in the main text , IgG results are shown for assays using NP as target antigen ( ELISA-NP) , and IgM results are shown for assays using the Spike antigen ( ELISA-Spike) , as these assays are most often used for the two antibody types ( Sethuraman et al. 2020; To et al. 2020) . NP as target antigen ( ELISA-NP) ,suggested: NoneDetection probability of IgG , IgM and NT ( neutralizing ) antibody ( A ) and RNA in different sample types ( B ) over time since symptom onset. NT ( neutralizing )suggested: NoneAntibody level kinetics Patterns of antibody level kinetics are relatively consistent across antibody type , assay and antigen ( Figure 3) . antigen ( Figure 3suggested: NoneIgG and IgM antibody level kinetics for different antibodies and assays. IgMsuggested: NoneDiscussion By leveraging multiple data sources on key aspects of the antibody response against SARSCoV-2, we were able to produce quantitative estimates of the mean and variation of seroconversion timing, antibody level kinetics, and the changes in antibody and RNA detection probability. SARSCoV-2suggested: NoneSoftware and Algorithms Sentences Resources Different combinations of search terms were used in order to maximize the likelihood of finding an article through Pubmed Pubmedsuggested: (PubMed, SCR_004846), Google Scholar , and medRxiv . Google Scholarsuggested: (Google Scholar, SCR_008878)Data were extracted from published material , and digitized from figures when necessary using WebPlotDigitizer ( Rohatgi 2019) . WebPlotDigitizersuggested: (WebPlotDigitizer, SCR_013996)All data preparation , cleaning , analysis and plotting was done in R version 3.6.1 ( R Core Team 2019 ) using packages ggplot2 ( Wickham 2016) , dplyr ( Wickham et al. 2019) , readxl ( Wickham & Bryan 2019) ggplot2suggested: (ggplot2, SCR_014601)Results from OddPub: Thank you for sharing your code.
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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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
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