Cumulative Active and Recovery Rates Based Criterion for Gradual Lockdown Exit: A Global Observation of SARS Cov-2 Management

  1. Dhananjay V Raje
  2. Abhay Bajaj
  3. Moumita Chakraborty
  4. Hemant J Purohit

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

On March 11, 2020 the World Health Organization (WHO) had declared SARS-CoV-2 pandemic; and at present there are over 6 million cases across the globe. Based on the interim guidelines of WHO, most of the countries opted for social distancing with lockdown as the only way to control the pandemic. This led to ‘Manufacturing’ shut down, which acted as a spanner in the wheel for international supply chain leading to pressure on governments to review the protocols of the lockdown. We studied epidemiological parameters for 18 countries and obtained crossover time point referring to cumulative case active and case recovery rates and the time point for the peak positive confirmation rate in a time window of 92 days; and linked with the respective governmental decisions. For countries awaiting crossover, time series non-linear models could be used for predicting the crossover point. A sample study was carried out for India. The median time for reaching crossover for 12 countries was 37 days, while peak positive confirmation rate was 30 days after their first intervention. These countries enforced strict lockdown regulations and have shown constant improvement in their recovery rate even after crossover time point. A phase wise relaxation of lockdown is evident after crossover point in most of these countries. The crossover time point with the subsequent increasing recovery rate can be a strategy for lockdown relaxation as evident from the experiences of few countries. Also, we propose a criterion based on 28 cumulative recovery and fatality rate for micro-management of lockdown.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.06.05.20123364: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    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: 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.

    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.

  2. Version published to 10.1101/2020.06.05.20123364 on medRxiv