The SARS-CoV-2 pandemic course in Saudi Arabia: A dynamic epidemiological model

This article has been Reviewed by the following groups

Read the full article

Abstract

Objective

Saudi Arabia ranks second in the number of coronavirus disease 2019 (COVID-19) cases in the Eastern Mediterranean region. It houses the two most sacred religious places for Muslims: Mecca and Medina. It is important to know what the trend in case numbers will be in the next 4–6 months, especially during the Hajj pilgrimage season.

Methods

Epidemiological data on COVID-19 were obtained from the Saudi Arabian Ministry of Health, the World Health Organization, and the Humanitarian Data Exchange. A susceptible-exposed-infectious-recovered (SEIR) prediction model was constructed to predict the trend in COVID-19 in Saudi Arabia in the next 6 months.

Findings

The model predicts that the number of active cases will peak by 20 May 2020. The cumulative infected cases are predicted to reach 59,663 at that time. The total number of infected individuals is estimated reach to 102,647 by the end of the pandemic.

Conclusion

Our estimates show that by the time the Hajj season commences in Saudi Arabia, the pandemic will be in the midst of its deceleration phase (phase 3). This information will likely be useful to policymakers in their management of the outbreak.

Article activity feed

  1. SciScore for 10.1101/2020.06.01.20119800: (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
    Microsoft Excel 2016 was used for data storage and management.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    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:
    On the other hand, one limitation of this study is that the accuracy of the predictions only lasts for a few weeks to months due to the erratic behavior of the current corona virus. Similarly, the accuracy of this prediction is highly correlated with how accurately and effectively new cases are recorded; it is vital to note that the number of identified infected cases will largely depend on the implemented testing strategy (i.e., how many tests are done in the population). As of May 28th, Saudi Arabia had done 770,696 tests, which is equal to approximately one test per 45 people(5). Additionally, our KSA-CoV-19 Model overestimates the number of recovered cases, but the number of recovered cases in Saudi Arabia has been increasing rapidly. For instance, in a period between 5 May and 15 May 2020, the number of recoveries increased 4-fold, and by May 28th, the number of recovered cases had surpassed the number of active cases by a large margin(5). Finally, despite utilizing previously published estimates for the average incubation period (e.dur) and average infection duration (i.dur), the model still undershoots the amount of active infection due to the rapid flow from the infected (I) compartment to the recovered (R) compartment of the SEIR model.

    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.