دوره 14، شماره 1 - ( 10-1403 )
جلد 14 شماره 1 صفحات 263-255 |
برگشت به فهرست نسخه ها
Ethics code: IR.SSU.SPH.REC.1400.169
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Miri M, Lotfi M, Falahzadeh H, Madadizadeh F. 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. JCHR 2025; 14 (1) :255-263
URL: http://jhr.ssu.ac.ir/article-1-1182-fa.html
URL: http://jhr.ssu.ac.ir/article-1-1182-fa.html
لطفی محمدحسن. 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. مجله تحقیقات سلامت. 1403; 14 (1) :255-263
چکیده: (177 مشاهده)
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.
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