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Change institution. Learn more. SARS-CoV-2 Speed up relief for long COVID through grassroots clinical trials Correspondence 27 FEB 24 Save lives in the next pandemic: ensure vaccine equity now Comment 23 FEB 24 Research funders must join the fight for equal access to medicines Editorial 30 JAN Beijing, China Institute of Physics IOP , Chinese Academy of Sciences CAS.
Faculty All Ranks Tenure track Professor in Neurocirtuitry of complex behavior The Department of neurosciences invites applications from candidates at all career levels who are interested in neurocircuitry related to productio Montréal, Quebec CA Université de Montréal.
Assistant Professor Tenure Track of Tissue Damage and Repair The Department of Biology www. Zurich, Switzerland ETH Zurich. Performance Analyst The role will support the business transition to open access through monitoring and analysing performance data.
London - hybrid working model Springer Nature Ltd. Close banner Close. This was based on the fact that RNA detection has been observed to be highly variable Wölfel et al. Respiratory sample types were classified as upper saliva, naso- or oropharyngeal or lower sputum, tracheal aspirate, bronchoalveolar lavage respiratory tract samples.
As RT-PCR protocols based on different target sequences resulted in similar sensitivities Sethuraman et al. To characterize the kinetics of antibody levels, we fit models to all individuals for whom longitudinal data were available i. at least three samples are available, one of which has to be positive.
Our goal was to estimate the rate of increase, and the timing and magnitude of the peak antibody level. Assays, antigens and reporting units differed extensively between studies, so antibody levels were normalized by dividing the level of each sample in a study by the maximum value observed in that study.
This allowed us to compare antibody level kinetic patterns between different studies. Antibody level normalization using scaling to a mean of zero and standard deviation of one resulted in the same patterns results not shown.
All time-series are shown in Appendix 1. As there were no or very limited data available for the later phase of kinetics, when antibody levels decay from their peak, we focused on the early phase of antibody increase up to peak level.
These early-phase dynamics follow a standard growth rate pattern, for which well-described functions are available. The asymptote a corresponds to the peak level, displacement b corresponds to the seroconversion time, and growth rate c corresponds with the antibody level increase rate.
Antibody levels y and a were log-transformed. We fit this function to the observed time series of normalized antibody levels using Bayesian Markov Chain Monte Carlo inference, using R-JAGS Plummer, All parameters were fit separately for each individual, with the assumption that they arise from the same population-level distribution, which was implemented as a hierarchical Bayesian model with hyperpriors for each parameter.
Posterior means of the parameters were used for further analyses and for plotting. Data were combined into subsets depending on the measure of interest assay, targeted antigen, disease severity.
Six parallel chains with different starting values were run for 70, burn-in iterations, of which the first 20, were discarded burn-in.
All data preparation, cleaning, analysis and plotting was done in R version 3. Welch two-sample t-tests were used to test for differences between estimated distributions. All codes used to fit models and produce results have been provided in Source code 1. The IDs shown in the figure legend correspond with the individual identification provided in the accompanying spreadsheet.
Antibody levels by days post symptom onset are shown for each individual top panel. Observed antibody levels bottom-left panel and fitted functions bottom-middle panel are shown for each individual, in addition to the overall mean black dashed line.
Chain convergence was assessed using the Gelman-Rubin diagnostic, and was one for all chains. In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses. This manuscript presents an important and timely assessment of current data on antibody kinetics and RNA shedding during SARS-CoV The researchers used innovative estimating procedures that draw from multiple data streams and data sources.
The characterization of detection probabilities of different tests over time is topical and very relevant for clinical use, public health and modeling and other research. Thank you for submitting your article "Quantifying antibody kinetics and RNA shedding during early-phase SARS-CoV-2 infection" for consideration by eLife.
Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Miles Davenport as the Senior Editor.
The following individual involved in review of your submission has agreed to reveal their identity: Michael J Mina Reviewer 3. The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.
First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv if it is not already there along with this decision letter and a formal designation that the manuscript is "in revision at eLife ".
Please let us know if you would like to pursue this option. If your work is more suitable for medRxiv, you will need to post the preprint yourself, as the mechanisms for us to do so are still in development. All reviewers expressed that they greatly enjoyed reading the paper and thought it is exactly the type of data integration that needs to be taking place in this fast paced and sparse data world that is COVID However, there were some methodological concerns that should be addressed in a revision as we felt they may influence the validity of the estimates in this paper:.
Please provide: a the full electronic search strategy for at least one database in appendix, with search terms used; b a flowchart of the study selection process, with inclusions and exclusions; c a summary table of all included studies, providing an overview of their main characteristics ex.
assay, antigen, countries, age range of participants, sample sizes, and maximum follow-up times ; d the full list of citations of all included studies. While this paper is not a formal systematic review, some guidance on how to present the elements listed above can be found in the PRISMA guidelines.
This means that the results on the fitting of antibody levels cannot be reliably interpreted and casts doubt on the validity of estimates from these analyses.
There are several individuals for which we observe very poor fits. Perhaps visualizing the uncertainty in the model estimates of antibody levels would clarify.
The chains need to be run again. Calculation of the Gelman-Rubin convergence statistic would provide evidence of chain convergence. The reviewers recommend running the chains for longer and re-tuning the proposal distributions in order to achieve chain convergence.
We have also added a flowchart Figure 5 and a table listing the key features of the selected articles Figure 5—source data 1. We fully agree, and have re-done all MCMC fitting using a better method JAGS instead of R Metropolis-Hastings MCMC.
Chains for all parameters now converge nicely, with clean posterior distributions. Additionally, the much improved model fits have motivated us to improve parameter estimation by implementing individual growth rates as opposed to one population-level growth rate previously.
These improvements did not change any qualitative results, and now allowed all statistical comparisons of model parameters to be done fully based on posterior distributions that take into account parameter uncertainty.
These changes resulted in the following edits in the main text and supplementary information:. Unfortunately a bug was found in the code in the process of improving the models, which previously resulted in wrong subsetting of IgM results of mild cases.
As a result, we now find a marginally significant difference in IgM antibody level growth rates and peak level timing between mild and severe cases. This did not have a major impact on the overall conclusions however, as the difference was not large, and seroconversion as well as antibody detection patterns that are based on much larger datasets do not provide evidence for a general effect of disease severity on antibody patterns.
The consensus patterns from our meta-analysis suggest that any interaction between disease severity and antibody response must be subtle and sensitive to other sources of variation, explaining the inconsistencies seen across studies". The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
BB was supported by the European Commission Horizon Marie Sklodowska-Curie Actions grant no. JOL-S and AG are supported by the Defense Advanced Research Projects Agency DARPA PREEMPT D18AC and the UCLA AIDS Institute and Charity Treks, and JOL-S and KCP are supported by the U.
Department of Defense and the Cooperative Ecosystem Studies Unit Cooperative Agreement WT The content of the article does not necessarily reflect the position or the policy of the U. government, and no official endorsement should be inferred.
This article is distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use and redistribution provided that the original author and source are credited. Article citation count generated by polling the highest count across the following sources: PubMed Central , Crossref , Scopus.
The aim of our study was to test the hypothesis that the community contact tracing strategy of testing contacts in households immediately instead of at the end of quarantine had an impact on the transmission of SARS-CoV-2 in schools in Reggio Emilia Province.
We analysed surveillance data on notification of COVID cases in schools between 1 September and 4 April Median tracing delay decreased from 7 to 3. Knowing the source of infection of the index case led to a decrease in secondary transmission IRR 0. Prompt contact testing in the community reduces the time of contact tracing and increases the ability to identify the source of infection in school outbreaks.
Although there are strong reasons for thinking it is a causal link, observed differences can be also due to differences in the force of infection and to other control measures put in place.
This project was carried out with the technical and financial support of the Italian Ministry of Health — CCM and Ricerca Corrente Annual Program In biomedical science, it is a reality that many published results do not withstand deeper investigation, and there is growing concern over a replicability crisis in science.
Recently, Ellipse of Insignificance EOI analysis was introduced as a tool to allow researchers to gauge the robustness of reported results in dichotomous outcome design trials, giving precise deterministic values for the degree of miscoding between events and non-events tolerable simultaneously in both control and experimental arms Grimes, While this is useful for situations where potential miscoding might transpire, it does not account for situations where apparently significant findings might result from accidental or deliberate data redaction in either the control or experimental arms of an experiment, or from missing data or systematic redaction.
To address these scenarios, we introduce Region of Attainable Redaction ROAR , a tool that extends EOI analysis to account for situations of potential data redaction. This produces a bounded cubic curve rather than an ellipse, and we outline how this can be used to identify potential redaction through an approach analogous to EOI.
Applications are illustrated, and source code, including a web-based implementation that performs EOI and ROAR analysis in tandem for dichotomous outcome trials is provided.
Share this article Doi. Cite this article Benny Borremans Amandine Gamble KC Prager Sarah K Helman Abby M McClain Caitlin Cox Van Savage James O Lloyd-Smith Quantifying antibody kinetics and RNA detection during early-phase SARS-CoV-2 infection by time since symptom onset. Full text Figures and data Side by side Abstract Introduction Results Discussion Materials and methods Appendix 1 Data availability References Decision letter Author response Article and author information Metrics.
Figure 1 with 5 supplements see all. Download asset Open asset. Figure 1—source data 1 Fitted normal distribution parameters for seroconversion time using different assays. Download elifefig1-data1-v2. Figure 2 with 4 supplements see all. Figure 2—source data 1 IgG ELISA-NP detection probability.
N: sample size including interpolated samples. Download elifefig2-data1-v2. Download elifefig2-data2-v2. Download elifefig2-data3-v2. Download elifefig2-data4-v2. Download elifefig2-data5-v2. Figure 3. docx Download elifefig3-data1-v2. Download elifefig3-data2-v2.
Download elifefig3-data3-v2. Download elifefig3-data4-v2. Table 1. Question What to test for Optimal timing to test Comments Importance Has an individual been exposed in the past? Transmission models Weitz et al. Is an individual currently infected?
Detection probability highest for lower respiratory tract or fecal samples, but upper respiratory tract samples are necessary to assess transmission potential. Assess transmission risk to others; contact tracing Giordano et al. How recently was an individual exposed?
Recent exposure is more likely correlated with transmission risk, and is a useful measure for prioritizing contact tracing, notably for asymptomatic cases Okba et al. Figure 4. Figure 5. Figure 5—source data 1 Key features of articles used for analysis.
Download elifefig5-data1-v2. Appendix 1—figure 1. Appendix 1—figure 2. Appendix 1—figure 3. Appendix 1—figure 4. Appendix 1—figure 5.
Appendix 1—figure 6. Appendix 1—figure 7.
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