Karimi Roya, Farrokhi Mehrdad, Izadi Neda, Ghajari Hadis, Khosravi Shadmani Fatemeh, Najafi Farid, Shakiba Ebrahim, Karami Manoochehr, Shojaeian Masoud, Moradi Ghobad, Ghaderi Ebrahim, Nouri Elham, Ahmadi Ali, Mohammadian Hafshejani Abdollah, Sartipi Majid, Zali Alireza, Bahadori Monfared Ayad, Davatgar Raha, Hashemi Nazari Seyed Saeed
Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway.
Student Research Committee, Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Arch Acad Emerg Med. 2024 Sep 5;12(1):e66. doi: 10.22037/aaem.v12i1.2376. eCollection 2024.
In infectious diseases, there are essential indices used to describe the disease state. In this study, we estimated the basic reproduction number, R0, peak level, doubling time, and daily growth rate of COVID-19.
This ecological study was conducted in 5 provinces of Iran. The daily numbers of new COVID-19 cases from January 17 to February 8, 2020 were used to determine the basic reproduction number (R0), peak date, doubling time, and daily growth rates in all five provinces. A sensitivity analysis was conducted to estimate epidemiological parameters.
The highest and lowest number of deaths were observed in Hamedan (657 deaths) and Chaharmahal and Bakhtiari (54 deaths) provinces, respectively. The doubling time of confirmed cases in Kermanshah and Hamedan ranged widely from 18.59 days (95% confidence interval (CI): 17.38, 20) to 76.66 days (95% CI: 56.36, 119.78). In addition, the highest daily growth rates of confirmed cases were observed in Kermanshah (0.037, 95% CI: 0.034, 0.039) and Sistan and Baluchestan (0.032, 95% CI: 0.030, 0.034) provinces.
In light of our findings, it is imperative to tailor containment strategies to the unique epidemiological profiles of each region in order to effectively mitigate the spread and impact of COVID-19. The wide variation in doubling times underscores the importance of flexibility in public health responses. By adapting measures to local conditions, we can better address the evolving dynamics of the pandemic and safeguard the well-being of communities.
在传染病中，有一些用于描述疾病状态的重要指标。在本研究中，我们估算了新型冠状病毒肺炎（COVID-19）的基本再生数R0、峰值水平、倍增时间和每日增长率。
本生态学研究在伊朗的5个省份开展。利用2020年1月17日至2月8日新型冠状病毒肺炎新增病例的每日数据，确定所有5个省份的基本再生数（R0）、峰值日期、倍增时间和每日增长率。进行敏感性分析以估算流行病学参数。
分别在哈马丹省（657例死亡）和恰哈马哈勒-巴赫蒂亚里省（54例死亡）观察到最高和最低死亡人数。克尔曼沙赫省和哈马丹省确诊病例的倍增时间差异很大，从18.59天（95%置信区间（CI）：17.38，20）到76.66天（95%CI：56.36，119.78）。此外，在克尔曼沙赫省（0.037，95%CI：0.034，0.039）和锡斯坦-俾路支斯坦省（0.032，95%CI：0.030，0.034）观察到确诊病例的最高每日增长率。
根据我们的研究结果，必须根据每个地区独特的流行病学特征制定防控策略，以有效减轻新型冠状病毒肺炎的传播和影响。倍增时间的广泛差异凸显了公共卫生应对措施灵活性的重要性。通过使措施适应当地情况，我们可以更好地应对疫情不断变化的动态，保障社区的福祉。
