Moradzadeh Rahmatollah, Jamalian Mohammad, Nazari Javad, Hosseinkhani Zahra, Zamanian Maryam
Department of Epidemiology, School of Health, Arak University of Medical Sciences, Arak, Iran.
Department of Forensic Medicine and Poisoning, Arak University of Medical Sciences, Arak, Iran.
J Res Med Sci. 2021 Sep 30;26:87. doi: 10.4103/jrms.JRMS_480_20. eCollection 2021.
The monitoring of reproduction number over time provides feedback on the effectiveness of interventions and on the need to intensify control efforts. Hence, we aimed to compute basic (R) and real-time (Rt) reproduction number and predict the trend and the size of the coronavirus disease 2019 (COVID-19) outbreak in the center of Iran.
We used the 887 confirmed cases of COVID-19 from February 20, 2020, to April 17, 2020 in the center of Iran. We considered three scenarios for serial intervals (SIs) with gamma distribution. R was calculated by the sequential Bayesian and time-dependent methods. Based on a branching process using the Poisson distributed number of new cases per day, the daily incidence and cumulative incidence for the next 30 days were predicted. The analysis was applied in R packages 3.6.3 and STATA 12.0.
The model shows that the R of COVID-19 has been decreasing since the onset of the epidemic. According to three scenarios based on different distributions of SIs in the past 58 days from the epidemic, R has been 1.03 (0.94, 1.14), 1.05 (0.96, 1.15), and 1.08 (0.98, 1.18) and the cumulative incidence cases will be 360 (180, 603), 388 (238, 573), and 444 (249, 707) for the next 30 days, respectively.
Based on the real-time data extracted from the center of Iran, R has been decreasing substantially since the beginning of the epidemic, and it is expected to remain almost constant or continue to decline slightly in the next 30 days, which is consequence of the schools and universities shutting down, reduction of working hours, mass screening, and social distancing.
对繁殖数随时间的监测可为干预措施的有效性以及加强防控力度的必要性提供反馈。因此，我们旨在计算伊朗中部2019年冠状病毒病（COVID-19）疫情的基本繁殖数（R）和实时繁殖数（Rt），并预测其趋势和规模。
我们使用了2020年2月20日至2020年4月17日伊朗中部887例COVID-19确诊病例。我们考虑了具有伽马分布的三种系列间隔（SI）情景。R通过序贯贝叶斯方法和时间依赖方法计算。基于使用每天新发病例的泊松分布数量的分支过程，预测未来30天的每日发病率和累积发病率。分析在R软件包3.6.3和STATA 12.0中进行。
该模型显示，自疫情爆发以来，COVID-19的R一直在下降。根据疫情爆发后过去58天基于不同SI分布的三种情景，R分别为1.03（0.94，1.14）、1.05（0.96，1.15）和1.08（0.98，1.18），未来30天的累积发病病例将分别为360（180，603）、388（238，573）和444（249，707）。
基于从伊朗中部提取的实时数据，自疫情开始以来R已大幅下降，预计在未来30天内将几乎保持不变或继续略有下降，这是学校和大学关闭、工作时间减少、大规模筛查和社交距离措施的结果。
