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Background <p>The COVID-19 pandemic, caused by SARS-CoV-2, began in December 2019 and severely impacted global healthcare systems, particularly in resource-limited regions like Kurdistan Province, Iran. During the first wave (February–April 2020), challenges such as limited infrastructure and incomplete data underscored the need for epidemiological modeling to guide public health responses.</p> Methods <p>This retrospective observational ecological study combined individual-level data from hospitalized patients with aggregate daily hospitalization counts to model the first COVID-19 wave in Kurdistan Province, Iran, using SEIR compartmental modeling and ARIMA time-series analysis. This study employed the SEIR model to estimate transmission (β) and recovery (γ) rates and used ARIMA (1) time series analysis to forecast hospitalization trends. Data were collected from 1,247 confirmed and suspected COVID-19 patients at Tohid and Kosar hospitals in Sanandaj, covering demographics (age, gender), clinical symptoms (fever, cough, shortness of breath, fatigue), comorbidities (diabetes, cardiovascular disease, hypertension, pulmonary disease), and outcomes (discharge, death, ICU stay). The SEIR model was solved using the Runge-Kutta method, and ARIMA was fitted to daily hospitalization data.</p> Results <p>The SEIR model predicted an epidemic peak around April 3, 2020, with approximately 8,000 active cases (0.5% of the 1.6&#xa0;million population). ARIMA analysis confirmed rising hospitalizations until early April, followed by a decline (MSE = 12.4). Patients had a mean age of 53.2 years (SD = 20.1), with 60.3% male. Common symptoms were fever (80.4%), cough (74.8%), and shortness of breath (65.2%); 42.1% had comorbidities. Outcomes included 69.8% discharged, 19.6% deceased, and 10.6% in ICU.</p> Conclusion <p>The SEIR and ARIMA models effectively described epidemic dynamics, highlighting the impact of social interventions. Enhanced data systems and healthcare infrastructure are critical for managing future epidemics in resource-constrained regions.</p>

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Modeling the COVID-19 epidemic in kurdistan province using the SEIR model and time series analysis of hospitalized patients during the first wave

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月30日

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  • Eghbal Zandkarimi

摘要

Background

The COVID-19 pandemic, caused by SARS-CoV-2, began in December 2019 and severely impacted global healthcare systems, particularly in resource-limited regions like Kurdistan Province, Iran. During the first wave (February–April 2020), challenges such as limited infrastructure and incomplete data underscored the need for epidemiological modeling to guide public health responses.

Methods

This retrospective observational ecological study combined individual-level data from hospitalized patients with aggregate daily hospitalization counts to model the first COVID-19 wave in Kurdistan Province, Iran, using SEIR compartmental modeling and ARIMA time-series analysis. This study employed the SEIR model to estimate transmission (β) and recovery (γ) rates and used ARIMA (1) time series analysis to forecast hospitalization trends. Data were collected from 1,247 confirmed and suspected COVID-19 patients at Tohid and Kosar hospitals in Sanandaj, covering demographics (age, gender), clinical symptoms (fever, cough, shortness of breath, fatigue), comorbidities (diabetes, cardiovascular disease, hypertension, pulmonary disease), and outcomes (discharge, death, ICU stay). The SEIR model was solved using the Runge-Kutta method, and ARIMA was fitted to daily hospitalization data.

Results

The SEIR model predicted an epidemic peak around April 3, 2020, with approximately 8,000 active cases (0.5% of the 1.6 million population). ARIMA analysis confirmed rising hospitalizations until early April, followed by a decline (MSE = 12.4). Patients had a mean age of 53.2 years (SD = 20.1), with 60.3% male. Common symptoms were fever (80.4%), cough (74.8%), and shortness of breath (65.2%); 42.1% had comorbidities. Outcomes included 69.8% discharged, 19.6% deceased, and 10.6% in ICU.

Conclusion

The SEIR and ARIMA models effectively described epidemic dynamics, highlighting the impact of social interventions. Enhanced data systems and healthcare infrastructure are critical for managing future epidemics in resource-constrained regions.