Analysis and Prediction of the COVID-19 outbreak in Pakistan

  1. Mohsin Ali
  2. Mudassar Imran
  3. Adnan Khan

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Abstract

In this study we estimate the severity of the COVID-19 outbreak in Pakistan prior to and after lock down restrictions were eased. We also project the epidemic curve considering realistic quarantine, social distancing and possible medication scenarios. We use a deterministic epidemic model that includes asymptomatic, quarantined, isolated and medicated population compartments for our analysis. We calculate the basic reproduction number ℛ 0 for the pre and post lock down periods, noting that during this time no medication was available. 1 The pre-lock down value of ℛ 0 is estimated to be 1.07 and the post lock down value is estimated to be 1.86. We use this analysis to project the epidemic curve for a variety of lock down, social distancing and medication scenarios. We note that if no substantial efforts are made to contain the epidemic, it will peak in mid of September, with the maximum projected active cases being close to 700,000. In a realistic, best case scenario, we project that the epidemic peaks in early to mid July with the maximum active cases being around 120000.We note that social distancing measures and medication if available will help flatten the curve, however without the reintroduction of further lock down it would be very difficult to bring ℛ 0 below 1. Our study strongly supports the recent WHO recommendation of reintroducing lock downs to control the epidemic.

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  1. ScreenIT

    SciScore for 10.1101/2020.06.21.20136341: (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 10.1101/2020.06.21.20136341 on medRxiv
  3. Version published to 10.1080/17513758.2020.1814883