不同估算方法在伊朗伊斯兰共和国SARS-CoV-2初始繁殖数的比较

Nasrin Talkhi, N. Esmaeilzadeh, M. Shakeri, Zahra Pasdar
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摘要

背景:基本繁殖数(R0)是表明疾病传播程度的流行阈值参数,可为规划控制措施提供建议。目的:本研究的目的是比较在SARS-CoV-2爆发早期估计R0的不同方法,并找出最合适的模型。方法:该数据集来自伊朗2020年2月26日至5月30日累计实验室确诊的COVID-19病例。采用指数增长(EG)率、最大似然(ML)、时间依赖(TD)繁殖数、攻击率(AR)和顺序贝叶斯(SB)模型等方法。连续间隔(SI)分布采用gamma分布(平均4.41±3.17天)。根据最小均方根误差(RMSE)选择最佳拟合方法。结果:我们得到的估计R0[95%置信区间]:1.55 [1.54;1.55], 1.46 [1.45;1.46], 1.31 [1.30;1.32], 1.40 [1.39;1.41]分别采用EG、ML、TD和SB方法。此外,EG和ML方法对R0的估计过高,而SB方法对R0的估计欠拟合。AR法估计R0等于1。TD方法的均方根误差最低。结论:TD的模拟R0和实际R0表明,该方法与实际数据拟合良好,RMSE最低。因此,在估计实际R0值时,TD方法是最合适的方法,性能最好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of Different Approaches in Estimating Initial Reproduction Number of SARS-CoV-2 in the Islamic Republic of Iran
Background: The basic reproduction number (R0) is an epidemic threshold parameter that indicates the magnitude of disease transmission and thus allows suggestions for the planning of control measures. Objectives: Our aim in this study was to compare different approaches for estimating R0 in the early stage of the SARS-CoV-2 outbreak and discern the best-fitting model. Methods: The dataset was derived from cumulative laboratory-confirmed COVID-19 cases from 26th February to 30th May 2020 in Iran. The methods of exponential growth (EG) rate, maximum likelihood (ML), time-dependent (TD) reproduction number, attack rate (AR), and sequential Bayesian (SB) model were used. The gamma distribution (mean 4.41 ± 3.17 days) was used for serial interval (SI) distribution. The best-fitting method was selected according to the lowest root mean square error (RMSE). Results: We obtained the following estimated R0 [95% confidence interval]: 1.55 [1.54; 1.55], 1.46 [1.45; 1.46], 1.31 [1.30; 1.32], and 1.40 [1.39; 1.41] using EG, ML, TD, and SB methods, respectively. Additionally, the EG and ML methods showed an overestimation of R0, and the SB method showed to be under-fitting in the estimation of R0. The AR method estimated R0 equal to one. The TD method had the lowest RMSE. Conclusions: The simulated and actual R0 of TD showed that this method had a good fit for actual data and the lowest RMSE. Therefore, the TD method is the most appropriate method with the best performance in estimating actual R0 values.
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