Nikbakht Roya, Baneshi Mohammad Reza, Bahrampour Abbas
Department of Biostatistics and Epidemiology, Modeling in Health Research Center, Faculty of Health, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
J Res Health Sci. 2018 Sep 22;18(4):e00427.
Determining the epidemic threshold parameter helps health providers calculate the coverage while guiding them in planning the process of vaccination strategy. Since the trend and mechanism of influenza is very similar in different countries, we planned a study with the mentioned goal by using data of US from 2017 to 2018.
A secondary study.
R0 and corresponding vaccination coverage are estimated using the national and state-level data of the US from the 40th in 2017 to the 5th week in 2018. Four methods maximum likelihood (ML), exponential growth (EG), time-dependent reproduction numbers (TD), and sequential Bayesian (SB) are used to calculate minimum vaccination coverage. The gamma distribution is considered as the distribution and the generation of time.
The peak of epidemy in most states has occurred in the 15th week after the beginning of the epidemics. The generation time obey the Gamma distribution with mean and standard deviation of 3.6 and 1.6, respectively, was utilized for the generation time. The R0 (vaccination coverage) equaled 1.94 (48.4%), 1.80 (44.4%), 3.06 (67.3%), and 2.11 (52.6%) for EG, ML, SB, and TD methods at the national level, respectively.
The R0 estimations were in the range of 1.8-3.06, indicating that an epidemic has occurred in the US (R0>1). Thus, it is required to vaccinate at least 44.4% to 67.3% to prevent the next epidemics of influenza. The findings of this study assist futures studies to apply disease control by vaccination strategies in order to prevent a national disaster.
确定流行阈值参数有助于医疗服务提供者计算疫苗接种覆盖率,同时指导他们制定疫苗接种策略。由于不同国家流感的流行趋势和机制非常相似,我们计划利用美国2017年至2018年的数据进行一项具有上述目标的研究。
二次研究。
利用美国2017年第40周至2018年第5周的国家和州层面数据,估计基本再生数(R0)和相应的疫苗接种覆盖率。使用最大似然法(ML)、指数增长法(EG)、时间依赖繁殖数法(TD)和序贯贝叶斯法(SB)四种方法计算最低疫苗接种覆盖率。将伽马分布视为时间分布和代时分布。
大多数州的疫情高峰出现在疫情开始后的第15周。代时服从伽马分布,均值和标准差分别为3.6和1.6,用于代时分析。在国家层面,EG、ML、SB和TD方法的R0(疫苗接种覆盖率)分别为1.94(48.4%)、1.80(44.4%)、3.06(67.3%)和2.11(52.6%)。
R0估计值在1.8 - 3.06范围内,表明美国已发生疫情(R0>1)。因此,需要至少44.4%至67.3%的疫苗接种率来预防下一次流感疫情。本研究结果有助于未来研究通过疫苗接种策略实施疾病控制,以防止全国性灾难。