Impact of lock down relaxation on the COVID-19 epidemic trajectory in Bangladesh

  1. Shafiun Nahin Shimul
  2. Mofakhar Hussain
  3. Abu Jamil Faisel
  4. Syed Abdul Hamid

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

In this projection exercise, we analyzed the circumstances of the COVID-19 pandemic in Bangladesh and used multiple methods to characterize the epidemic curve. We merged several publicly available data sets for the purpose. Projections using actual Government data as of June 16, 2020 reveals that the epidemic curve for Bangladesh may be different from that of developed countries and quite similar to such curves in countries in the region. This is true, both in terms of incidence of cases (total number of cases per million population) and length of the epidemic (months to peak or flatten the epidemic curve). We find that while Bangladesh went into lockdown early, efforts to maintain lockdown at a national level was relaxed and new cases accelerated; with significant growth happening since lifting of lockdown on May 31. Our estimates indicate prevalence of COVID-19 may be between 200,000 and 600,000 towards end of the year, may take 9 months (270 days) to flatten the epidemic curve, lifting of the lockdown may have increased total cases by 60 to 100% and may have prolonged the epidemic by additional 2-3 months.

Article activity feed

  1. Version published to 10.1101/2020.07.20.20158527v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.07.20.20158527: (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
    SentencesResources
    The prophet model can be run using R or python.
    python
    suggested: (IPython, RRID:SCR_001658)

    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:
    This study has several limitations. The use of multiple models to predict the outcome of an epidemic in a single country may result in outcome that varies significantly from one another, thus increasing the level of uncertainty. Limited data availability of Bangladesh specific transmission and recovery rates forces us to select initial values that may not be appropriate. Time specific influential events such holidays, major climate events and subnational events could not be captured in any of the models. Some of these events may extend the epidemic period. This study also does not take into account recent changes in policy such as assignment of red zone, lock down of neighborhoods, increases in contact tracing and other measures that government has taken to mitigate the spread of the virus. This study does not address the direct and indirect health effects of the COVID-19 epidemic. Future analysis could address these shortcomings. There may also be a need to better understand and apply measures not directly captured in epidemiological reporting; we may need to include sociological and anthropological considerations in localities to better address the appropriateness of measures and methods in our modelling approach (37).

    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.
    • Thank you for including a protocol registration statement.

    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.

  3. Version published to 10.1101/2020.07.20.20158527v1 on medRxiv