Transmission dynamics of the COVID-19 epidemic in India and modeling optimal lockdown exit strategies

This article has been Reviewed by the following groups

Read the full article

Abstract

No abstract available

Article activity feed

  1. SciScore for 10.1101/2020.05.13.20096826: (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: Thank you for sharing your code and data.


    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Several limitations of our study should be noted. First, the estimation of reproductive numbers is based upon detected cases which are only a fraction of the actual infections, and we do not account for variation in detection which if significant, may confound the changes in estimated Rt over time. Second, we assumed the delay from symptom onset to confirmation to be similar to the delay from symptom onset to hospitalisation due to lack of data, and that this delay is uniform across India. Though the latter approximates the former, further studies to ascertain the true reporting lag in India are needed since it is critical for identifying Rt changes at correct points in time.6 Third, our model does not factor in pre-symptomatic transmission, which along with a short estimated reporting lag may underestimate the transmission by symptomatic cases and thus inflate the effect of detecting asymptomatics. Fourth, since our primary goal was to evaluate the effect of identifying asymptomatics, we assumed that increased testing increases the detection of asymptomatics only, while in reality it would detect more cases across the clinical spectrum. However, the interpretations regarding impact of increased testing are not sensitive to this assumption, which has been discussed through an alternate interpretation of the model (appendix p4). Fifth, we assumed a constant death rate (κ), in contrast to reality where the death rate gradually decreases during an epidemic to ultimately converge...

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).


    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.