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Prediction of peak and termination of novel coronavirus COVID-19 epidemic in Iran

Author

Listed:
  • Sepehr Rafieenasab

    (Department of Mechanical Engineering, Ferdowsi University of Mashhad, P.O.B. 91775-1111 Mashhad, Iran)

  • Amir-Pouyan Zahiri

    (Department of Mechanical Engineering, Ferdowsi University of Mashhad, P.O.B. 91775-1111 Mashhad, Iran)

  • Ehsan Roohi

    (Department of Mechanical Engineering, Ferdowsi University of Mashhad, P.O.B. 91775-1111 Mashhad, Iran†State Key Laboratory for Strength and Vibration of Mechanical Structures, International Center for Applied Mechanics (ICAM), School of Aerospace Engineering, Xi’an Jiaotong University (XJTU), Xi’an, P. R. China)

Abstract

The growth and development of COVID-19 transmission have significantly attracted the attention of many societies, particularly Iran, that have been struggling with this contagious, infectious disease since late February 2020. In this study, the known “Susceptible-Infectious-Recovered (SIR)” and some other mathematical approaches were used to investigate the dynamics of the COVID-19 epidemic to provide a suitable assessment of the COVID-19 virus epidemic in Iran. The epidemic curve and SIR model parameters were obtained with the use of Iran’s official data. The recovered people were considered alongside the official number of confirmed victims as the reliable long-time statistical data. The results offer important predictions of the COVID-19 virus epidemic such as the realistic number of victims, infection rate, peak time and other characteristics. Besides, the effectiveness of infection and immunization rates to the number of infected people and epidemic end time are reported. Finally, different suggestions for decreasing victims are offered.

Suggested Citation

  • Sepehr Rafieenasab & Amir-Pouyan Zahiri & Ehsan Roohi, 2020. "Prediction of peak and termination of novel coronavirus COVID-19 epidemic in Iran," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 31(11), pages 1-17, November.
    • Handle: RePEc:wsi:ijmpcx:v:31:y:2020:i:11:n:s0129183120501521
    DOI: 10.1142/S0129183120501521
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    Citations

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    Cited by:

    1. Gregory L Watson & Di Xiong & Lu Zhang & Joseph A Zoller & John Shamshoian & Phillip Sundin & Teresa Bufford & Anne W Rimoin & Marc A Suchard & Christina M Ramirez, 2021. "Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-20, March.
    2. Ba Chu & Shafiullah Qureshi, 2020. "Predicting the COVID-19 pandemic in Canada and the US," Economics Bulletin, AccessEcon, vol. 40(3), pages 2565-2585.
    3. Qingqing Ji & Xu Zhao & Hanlin Ma & Qing Liu & Yiwen Liu & Qiyue Guan, 2021. "Estimation of COVID-19 Transmission and Advice on Public Health Interventions," Mathematics, MDPI, vol. 9(22), pages 1-18, November.

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