Application of Time-Series SIR Models in Analyzing the COVID-19 Pandemic in Iran: A Case Study of Data from February 2020 to December 2023

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