Estimation of the incubation period of COVID-19 using viral load data

This article has been Reviewed by the following groups

Read the full article

Abstract

No abstract available

Article activity feed

  1. SciScore for 10.1101/2020.06.16.20132985: (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: We detected the following sentences addressing limitations in the study:
    The strength of this approach is that it can complement limitations that classical interview-based approach has pertained to ascertain the exposure event. Our proposed approach may be applicable not only to the human infectious disease and zoonoses such as influenza and COVID-19, but to animal/livestock infectious diseases such as foot and mouth disease when contact recall is not possible. We note that there are several studies proposed statistical approach to estimate the incubation period using observed biomarkers, especially for HIV/AIDS. Shi et al. and Geskus used CD4 counts to estimate the incubation period as well as residual time (i.e., time from AIDS diagnosis to current time)(9, 10). The uniqueness of our approach compared with these examples is that we have used conventional viral dynamics model for respiratory diseases with acute course of illness. Limitations should be noted. Our approach did not account for any uncertainty in reporting of symptoms, which was accounted in the previous approach by Reich et al. (8). Combining ours with Reich et al. might reduce uncertainly surrounding the precise reporting of exposure and illness onset events. The model we used in this study did not include detailed immune response or antiviral effects given limited information. The proposed approach requires collection of viral loads over time since symptom onset, which might not be feasible for all patients or in resource limited contexts. Additional diagnostic testing methods are...

    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.

    About SciScore

    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 checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.