Estimation of the Basic Reproduction Number and Vaccination Coverage of Influenza in the United States (2017-18)

被引:0
作者
Nikbakht, Roya [1 ]
Baneshi, Mohammad Reza [1 ]
Bahrampour, Abbas [1 ]
机构
[1] Kerman Univ Med Sci, Inst Futures Studies Hlth, Fac Hlth, Dept Biostat & Epidemiol,Modeling Hlth Res Ctr, Kerman, Iran
关键词
Basic reproduction number; Epidemic threshold parameters; Vaccination coverage; Influenza;
D O I
暂无
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Background: 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. Study design: A secondary study. Methods: R-0 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. Results: 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 R-0 (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. Conclusion: The R-0 estimations were in the range of 1.8-3.06, indicating that an epidemic has occurred in the US (R-0>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.
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