Moser Carlee B, Gupta Mayetri, Archer Brett N, White Laura F
Department of Biostatistics, Boston University School of Public Health, Boston University, Boston, Massachusetts, United States of America.
National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg, South Africa.
PLoS One. 2015 Mar 20;10(3):e0118762. doi: 10.1371/journal.pone.0118762. eCollection 2015.
The basic reproductive number (R₀) and the distribution of the serial interval (SI) are often used to quantify transmission during an infectious disease outbreak. In this paper, we present estimates of R₀ and SI from the 2003 SARS outbreak in Hong Kong and Singapore, and the 2009 pandemic influenza A(H1N1) outbreak in South Africa using methods that expand upon an existing Bayesian framework. This expanded framework allows for the incorporation of additional information, such as contact tracing or household data, through prior distributions. The results for the R₀ and the SI from the influenza outbreak in South Africa were similar regardless of the prior information (R0 = 1.36-1.46, μ = 2.0-2.7, μ = mean of the SI). The estimates of R₀ and μ for the SARS outbreak ranged from 2.0-4.4 and 7.4-11.3, respectively, and were shown to vary depending on the use of contact tracing data. The impact of the contact tracing data was likely due to the small number of SARS cases relative to the size of the contact tracing sample.
基本再生数(R₀)和传播间隔(SI)的分布常用于量化传染病暴发期间的传播情况。在本文中,我们采用了在现有贝叶斯框架基础上进行扩展的方法,给出了2003年香港和新加坡SARS疫情以及2009年南非甲型H1N1流感大流行疫情的R₀和SI估计值。这个扩展框架允许通过先验分布纳入额外信息,如接触者追踪或家庭数据。无论先验信息如何,南非流感疫情的R₀和SI结果相似(R₀ = 1.36 - 1.46,μ = 2.0 - 2.7,μ为SI的均值)。SARS疫情的R₀和μ估计值分别为2.0 - 4.4和7.4 - 11.3,并且显示出会因接触者追踪数据的使用情况而有所不同。接触者追踪数据产生影响可能是因为相对于接触者追踪样本规模而言,SARS病例数量较少。