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俄亥俄州一个阿米什社区麻疹暴发期间公共卫生应对措施的影响:传播动力学建模。

Impact of Public Health Responses During a Measles Outbreak in an Amish Community in Ohio: Modeling the Dynamics of Transmission.

机构信息

Division of Viral Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia.

Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.

出版信息

Am J Epidemiol. 2018 Sep 1;187(9):2002-2010. doi: 10.1093/aje/kwy082.

Abstract

We quantified measles transmissibility during a measles outbreak in Ohio in 2014 to evaluate the impact of public health responses. Case incidence and the serial interval (time between symptom onset in primary cases and secondary cases) were used to assess trends in the effective reproduction number R (the average number of secondary cases generated per case). A mathematical model was parameterized using early R values to determine the size and duration of the outbreak that would have occurred if containment measures had not been initiated, as well as the impact of vaccination. As containment started, we found a 4-fold decline in R (from approximately 4 to 1) over the course of 2 weeks and maintenance of R < 1 as control measures continued. Under a conservative scenario, the model estimated 8,472 cases (90% confidence interval (CI): 8,447, 8,489) over 195 days (90% CI: 179, 223) without control efforts and 715 cases (90% CI: 103, 1,338) over 128 days (90% CI: 117, 139) when vaccination was included; 7,757 fewer cases (90% CI: 7,130, 8,365) and 67 fewer outbreak days (90% CI: 48, 98) were attributed to vaccination. Vaccination may not account entirely for transmission reductions, suggesting that changes in community behavior (social distancing) and other control efforts (isolation, quarantining) are important. Our findings highlight the benefits of measles outbreak response and of understanding behavior change dynamics.

摘要

我们量化了 2014 年俄亥俄州麻疹暴发期间的麻疹传播力,以评估公共卫生应对措施的影响。病例发病率和序列间隔(首发病例和次发病例症状出现之间的时间)用于评估有效繁殖数 R(每个病例产生的次级病例数的平均值)的趋势。使用早期 R 值对数学模型进行参数化,以确定如果不采取控制措施,暴发的规模和持续时间,以及接种疫苗的影响。随着控制措施的开始,我们发现 R 值在 2 周内下降了 4 倍(从大约 4 下降到 1),并且随着控制措施的继续,R 值保持在 1 以下。在保守的情况下,模型估计在没有控制措施的情况下,195 天(90%置信区间(CI):8447,8489)将发生 8472 例(90%CI:8412,8533),而在接种疫苗的情况下,128 天(90%CI:103,1338)将发生 715 例(90%CI:103,1338);接种疫苗可减少 7757 例(90%CI:7130,8365),减少 67 天(90%CI:48,98)。接种疫苗可能并不能完全解释传播减少的原因,这表明社区行为(社交距离)和其他控制措施(隔离、检疫)的变化很重要。我们的研究结果强调了麻疹暴发应对措施和了解行为变化动态的好处。

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