Modeling COVID-19 in Iran using Particle Swarm Optimization algorithm

  1. Ebrahim Sahafizadeh
  2. MohammadAli Khajeian

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Abstract

Background

The first confirmed cases of COVID-19 in Iran were reported on February 19, 2020. The coronavirus expanded rapidly in all Iranian provinces and three waves of COVID-19 cases have been observed since the pandemic took effect and the fourth wave of Covid-19 cases will likely be observed soon. This study aimed to model the spread of COVID-19 in Iran and to estimate the epidemic parameters and to predict the short-term future trend of COVID-19 in Iran.

Methods

We proposed a modified SEIR epidemic spreading model and we used data from February 20, 2020, to April 9, 2021, on the number of cases reported by Iranian governments to fit the proposed model on the reported data. Particle Swarm Optimization (PSO) algorithm was employed to estimate the parameters of the proposed model and the numerical simulation results were obtained by Runge-Kutta method. The estimated parameters were employed to calculate the effective reproduction number and to predict the short-term future trends of COVID-19 cases.

Results

The results indicated that the effective reproduction number has increased during Nowruz (Persian New Year) and it was estimated to be 1.28. Considering only two exposed cases as the initial cases in the model, the cumulative number of exposed cases was estimated to be 15,252,372 individuals since the beginning of the outbreak. The prediction of the short-term future trends of COVID-19 cases with different scenarios showed that another peak of the pandemic cases occurs in the next weeks. By immediate lockdown implementation the number of active infected cases was estimated to be 397,585.

Conclusion

Different scenarios of short-term prediction of the future trends of COVID-19 cases indicated that immediate strict social distancing policies need to be implemented to prevent a tremendous burden of the fourth major wave of COVID-19 infections on the health care system of Iran.

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  1. ScreenIT

    SciScore for 10.1101/2021.04.10.21255244: (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
    D) Error estimation: The fitting of the proposed model to the data is done in MATLAB by minimizing the root mean square error (RMSE) as the following:

    We used the daily reported data as the values of y and model generated data as the values of ŷ to calculate the error.

    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    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:
    There are some limitations in our study. First, the proposed model was set up based on a number of necessary assumptions. For example, we assumed that officially confirmed cases do not spread the disease after confirmation due to their contact limitations; we also assumed that the duration to be confirmed or transmitting to removed class due to self-quarantine are equal for exposed class. Second, the accuracy of the estimated exposed cases depends on the initial parameters such as the number of initially exposed cases which is a limitation. And finally, in term of prediction, our model uses the previous estimated parameters, assuming that the previous patterns continue in the future. However, the authorities often change their control strategies and the actual parameters significantly depend on these strategies which can lead to errors in predictions of the 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

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  2. Version published to 10.1101/2021.04.10.21255244v1 on medRxiv