How to Flatten the post-lockdown epidemic trajectory

  1. Roschen Sasikumar
  2. Ajit Haridas

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Populations are locked down during an epidemic to slow down the rate of infection so that epidemic trajectory is "flattened". This helps to keep cases at a manageable level. Given the enormous economic damage and misery caused by a lockdown, it is imperative to keep the lockdown period limited. A lockdown is useful only if it can be ensured that after the lockdown is lifted, the epidemic trajectory does not rise sharply again. We present here the results from a mathematical model of the epidemic which examines how the timing, strength and duration of the lockdown affects the post-lockdown epidemic trajectory. Our results show the following:

  • A early lockdown (imposed when less than 1% of the population has been infected), of any reasonable duration, cannot prevent the return of the epidemic when the lockdown is lifted. The curve starts climbing soon after lifting the lockdown and reaches a peak of the same height as the no-lockdown curve

  • The post-lockdown trajectory can be flattened only if the lockdown is imposed after about 10% of the population has recovered after infection.

  • The slope of the post-lockdown epidemic curve depends on the level of immunity built up in the population before and during the lockdown period. Application of lockdown around the inflexion point of the epidemic curve (the point of maximum slope of the curve) ensures that the post-lockdown curve is also flattened.

    • Article activity feed

    1. ScreenIT

      SciScore for 10.1101/2020.05.06.20093104: (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 10.1101/2020.05.06.20093104 on medRxiv