Comparison of the Effective Reproduction Number (Rt) Estimation Methods of COVID-۱۹ Using Simulation Data Based on Available Data from Iran, USA, UK, India, and Brazil

سال انتشار: 1401
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 125

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شناسه ملی سند علمی:

JR_JRHSU-22-3_003

تاریخ نمایه سازی: 28 تیر 1402

چکیده مقاله:

Background: Accurate determination of the effective reproduction number (Rt) is a very important strategy in the epidemiology of contagious diseases, including coronavirus disease ۲۰۱۹ (COVID-۱۹). This study compares different methods of estimating the Rt of susceptible population to identify the most accurate method for estimating Rt. Study Design: A secondary study. Methods: The value of Rt was estimated using attack rate (AR), exponential growth (EG), maximum likelihood (ML), time-dependent (TD), and sequential Bayesian (SB) methods, for Iran, the United States, the United Kingdom, India, and Brazil from June to October ۲۰۲۱. In order to accurately compare these methods, a simulation study was designed using forty scenarios. Results: The lowest mean square error (MSE) was observed for TD and ML methods, with ۱۵ and ۱۲ cases, respectively. Therefore, considering the estimated values of Rt based on the TD method, it was found that Rt values in the United Kingdom (۱.۳۳; ۹۵% CI: ۱.۱۴-۱.۵۲) and the United States (۱.۲۵; ۹۵% CI: ۱.۱۲-۱.۳۸) substantially have been more than those in other countries, such as Iran (۱.۰۷; ۹۵% CI: ۰.۹۵-۱.۱۹), India (۰.۹۹; ۹۵% CI: ۰.۸۹-۱.۰۸), and Brazil (۰.۹۸; ۹۵% CI: ۰.۸۴-۱.۱۴) from June to October ۲۰۲۱. Conclusion: The important result of this study is that TD and ML methods lead to a more accurate estimation of Rt of population than other methods. Therefore, in order to monitor and determine the epidemic situation and have a more accurate prediction of the incidence rate, as well as control COVID-۱۹ and similar diseases, the use of these two methods is suggested to more accurately estimate Rt.

کلیدواژه ها:

COVID-۱۹ ، Effective reproduction number (Rt) ، Maximum likelihood estimation ، Time-dependent ، Simulation

نویسندگان

Ali Karamoozlan

Ph.D, Department of Biostatistics and Epidemiology, Kerman University of Medical Sciences, Kerman, Iran- Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran

Abbas Bahrampour

Ph.D, Ph.D, Department of Biostatistics and Epidemiology, Kerman University of Medical Sciences, Kerman, Iran- Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran- Adjunct Profes