COVID 19: An SEIR model predicting disease progression and healthcare outcomes for Pakistan

  1. Ejaz Ahmad Khan
  2. Maida Umar
  3. Maryam Khalid

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Abstract

Background

Recent pandemic of the Noval Coronal Virus (COVID 19) has claimed more than 200,000 lives and about 3.8 million infected worldwide. Countries are being gradually exposed to its devastating threat without being properly prepared and with inadequate response. COVID 19’s first two cases were reported in Pakistan on February 26, 2020. We present a model depicting progression of epidemiology curve for Pakistan with and without interventions in view of its health system’ response capacity in near future.

Methodology

We used a modified compartmental epidemiological SEIR model to describe the outbreak of COVID-19 in Pakistan including the possibility of asymptomatic infection and presymptomatic transmission. The behavior of the dynamic model is determined by a set of clinical parameters and transmission rate.

Results

We estimated that in the absence of a set of proven interventions, the total susceptible population would be 43.24 million, exposed individuals would be almost 32 million, asymptomatic cases would be 13.13 million, mildly infected 30.64 million, severely infected slightly more than 6 million and critical cases would be around 967,000 in number. By that time, almost 760,000 fatalities of infected critical would have taken place. Comparing with the healthcare capacity of Pakistan, if we could “flatten the curve” to a level below the dashed grey line, the healthcare system will be capable of managing the cases with ideal healthcare facilities, where the grey line representing the healthcare capacity of Pakistan. With the intervention in place, the number of symptomatic infected individuals is expected to be almost 20 million.

Conclusion

We consider the impact of intervention and control measures on the spread of COVID-19 with 30% reduction in transmission from mild cases in case a set of interventions are judiciously in place to mitigate its impact.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.29.20116517: (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: 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.

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    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.05.29.20116517v1 on medRxiv