Prediction of the virus incubation period for COVID-19 and future outbreaks
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Abstract
A crucial factor in mitigating respiratory viral outbreaks is early determination of the duration of the incubation period and, accordingly, the required quarantine time for potentially exposed individuals. Here, we explore different genomic features of RNA viruses that correlate with the incubation times and provide a predictive model that accurately estimates the upper limit incubation time for diverse viruses including SARS-CoV-2, and thus, could help control future outbreaks.
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SciScore for 10.1101/2020.05.19.104513: (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 Elastic net was chosen because it has characteristics of both LASSO and Ridge regression, which are controlled by the penalties coefficients, thus outperforming other regularization and variable selection approaches34. LASSOsuggested: (LaSSO, RRID:SCR_003418)The elastic net model was constructed in Python using the scikit-learn35 ElasticNet function with default parameters. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was …SciScore for 10.1101/2020.05.19.104513: (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 Elastic net was chosen because it has characteristics of both LASSO and Ridge regression, which are controlled by the penalties coefficients, thus outperforming other regularization and variable selection approaches34. LASSOsuggested: (LaSSO, RRID:SCR_003418)The elastic net model was constructed in Python using the scikit-learn35 ElasticNet function with default parameters. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code.
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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