Hybrid modeling of COVID-19 progression in Iran‎: ‎Active case estimation from February 2020 to March 2022

Document Type : Original Scientific Paper

Authors

1 Department of Statistics‎, ‎Faculty of Mathematical Sciences‎, ‎Ferdowsi University of Mashhad‎, ‎Mashhad‎, ‎Iran

2 Department of Biostatistics‎, ‎School of Health‎, ‎Mashhad University of Medical Sciences‎, ‎Mashhad‎, ‎Iran

3 Department of Medical Laboratory Sciences‎, ‎Kashmar Faculty of Medical Sciences‎, ‎Mashhad University of Medical Sciences‎, ‎Iran

Abstract

The COVID-19 pandemic has unfolded in multiple waves across Iran‎, ‎presenting complex challenges for public health management‎. ‎Accurate modeling of active cases is essential for understanding transmission dynamics and guiding interventions‎. This study aims to investigate the progression of active COVID-19 cases across six epidemic waves in Iran using statistical‎, ‎machine learning‎, ‎and mathematical models to estimate key transmission rates and evaluate model performance‎. ‎‎Models including autoregressive integrated moving average‎, multilayer perceptron‎, ‎Holt-Winter‎, trigonometric seasonal‎, ‎Prophet‎, ‎and Bayesian structural time series were evaluated using root mean square, mean absolute, and mean absolute percentage errors‎. ‎Mathematical approaches were applied to estimate transmission rates‎: ‎infected to active‎, ‎active to recovered‎, ‎and active to death‎. ‎Comparative performance was assessed across all six waves‎. Model performance varied by wave and variable‎. Multilayer perceptron and Bayesian structural time series showed superior accuracy for infected and active cases‎, ‎respectively‎, ‎while autoregressive integrated moving average excelled in predicting recoveries and deaths‎. ‎Felberg consistently outperformed other models in estimating death cases‎. ‎Transmission rates revealed that ‎infected to active peaked during the third and fourth waves and declined post-vaccination‎. ‎The fifth wave showed increased ‎active to recovered and reduced active to death‎, ‎indicating improved recovery and reduced mortality‎. Combining statistical‎, ‎machine learning‎, ‎and mathematical models offers a robust framework for analyzing COVID-19 dynamics‎. ‎Transmission rate estimation provides actionable insights for epidemic control‎, ‎highlighting the impact of vaccination and public health compliance on disease progression.

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References

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Journal of Statistical Modelling: Theory and Applications
Volume 6, Issue 2
July 2025
Pages 101-115

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History

  • Receive Date: 29 September 2025
  • Revise Date: 30 May 2026
  • Accept Date: 07 June 2026

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