1- Center for Healthcare Data Modeling, Departments of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
2- 1.Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran 2.Center for Healthcare Data Modeling, Departments of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran ,madadizadehfarzan@gmail.com
2- 1.Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran 2.Center for Healthcare Data Modeling, Departments of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran ,
Abstract: (14 Views)
Background: The COVID-19 pandemic underscored the critical need for advanced modeling approaches to elucidate transmission dynamics and inform public health strategy. This study employed a Time-Series Susceptible-Infected-Recovered (TSIR) model to quantitatively analyze the pandemic trajectory in Iran and estimate the time-varying basic reproduction number (R₀) from February 2020 to December 2023.
Methods: In an analytical cross-sectional study, comprehensive national COVID-19 data were obtained from the Iranian Ministry of Health and validated international repositories. The TSIR framework was implemented using R software (v4.0.0) to estimate transmission parameters (β, γ) and reconstruct epidemic dynamics. Vaccination impact was assessed through comparative analysis of compartmental populations pre- and post-vaccination deployment.
Results: Analysis of 1,373 surveillance days revealed 7,625,160 confirmed cases with 146,741 fatalities (CFR: 2%). The TSIR model demonstrated superior tracking of seven distinct epidemic waves, with R₀ estimates declining to 0.2 during 2022-2023. Statistical analysis confirmed significant compartmental shifts post-vaccination (p<0.001), indicating substantial intervention impact. Moreover, model validation showed robust performance across multiple epidemic phases.
Conclusion: The TSIR model provides a validated framework for epidemic monitoring and evaluation of public health interventions in Iran. The sub-critical R₀ values observed during the study's conclusion reflect successful containment through combined vaccination and control measures. Therefore, integration of time-series epidemiological modeling into national surveillance systems is recommended for enhanced preparedness against future infectious disease threats.
Methods: In an analytical cross-sectional study, comprehensive national COVID-19 data were obtained from the Iranian Ministry of Health and validated international repositories. The TSIR framework was implemented using R software (v4.0.0) to estimate transmission parameters (β, γ) and reconstruct epidemic dynamics. Vaccination impact was assessed through comparative analysis of compartmental populations pre- and post-vaccination deployment.
Results: Analysis of 1,373 surveillance days revealed 7,625,160 confirmed cases with 146,741 fatalities (CFR: 2%). The TSIR model demonstrated superior tracking of seven distinct epidemic waves, with R₀ estimates declining to 0.2 during 2022-2023. Statistical analysis confirmed significant compartmental shifts post-vaccination (p<0.001), indicating substantial intervention impact. Moreover, model validation showed robust performance across multiple epidemic phases.
Conclusion: The TSIR model provides a validated framework for epidemic monitoring and evaluation of public health interventions in Iran. The sub-critical R₀ values observed during the study's conclusion reflect successful containment through combined vaccination and control measures. Therefore, integration of time-series epidemiological modeling into national surveillance systems is recommended for enhanced preparedness against future infectious disease threats.
Keywords: COVID-19, Basic Reproduction Number, Time Series Analysis, SIR Model, Iran, Vaccination Coverage, Epidemiological Modeling
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
