Dose-response modelling of endemic coronavirus and SARS-CoV-2: human challenge trials reveal the individual variation in susceptibility
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Abstract
We propose a mathematical framework to analyze and interpret the outcomes of human challenge trials. We present plausible infection risks with HCoV-229E and SARS-CoV-2 over a wide range of infectious dose, and suggest ways to improve the design of future trials and to translate its outcomes to the general population.
One sentence summary
We rephrase dose-response models in terms of heterogeneity in susceptibility in order to present the possible range of infection risks for endemic coronaviruses and SARS-CoV-2
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SciScore for 10.1101/2022.04.07.22273549: (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 Sentences Resources We conducted literature search using PubMed and Google Scholar, and 13 human challenge studies were found in total. PubMedsuggested: (PubMed, RRID:SCR_004846)Google Scholarsuggested: (Google Scholar, RRID:SCR_008878)For this computation we used the optim() function in the R statistical programming environment version 3.5.1., and 95 % confidence intervals were computed from 1000 bootstrapped samples. R statistical programming environmentsuggested: NoneResults from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: An explicit section about the limitations …SciScore for 10.1101/2022.04.07.22273549: (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 Sentences Resources We conducted literature search using PubMed and Google Scholar, and 13 human challenge studies were found in total. PubMedsuggested: (PubMed, RRID:SCR_004846)Google Scholarsuggested: (Google Scholar, RRID:SCR_008878)For this computation we used the optim() function in the R statistical programming environment version 3.5.1., and 95 % confidence intervals were computed from 1000 bootstrapped samples. R statistical programming environmentsuggested: NoneResults from OddPub: Thank you for sharing your code and data.
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.
Results from scite Reference Check: We found no unreliable references.
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