Wu Kendra M, Riley Steven
School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR.
Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom.
PLoS One. 2016 Feb 5;11(2):e0148061. doi: 10.1371/journal.pone.0148061. eCollection 2016.
Accurately assessing the transmissibility and serial interval of a novel human pathogen is public health priority so that the timing and required strength of interventions may be determined. Recent theoretical work has focused on making best use of data from the initial exponential phase of growth of incidence in large populations.
We measured generational transmissibility by the basic reproductive number R0 and the serial interval by its mean Tg. First, we constructed a simulation algorithm for case data arising from a small population of known size with R0 and Tg also known. We then developed an inferential model for the likelihood of these case data as a function of R0 and Tg. The model was designed to capture a) any signal of the serial interval distribution in the initial stochastic phase b) the growth rate of the exponential phase and c) the unique combination of R0 and Tg that generates a specific shape of peak incidence when the susceptible portion of a small population is depleted.
Extensive repeat simulation and parameter estimation revealed no bias in univariate estimates of either R0 and Tg. We were also able to simultaneously estimate both R0 and Tg. However, accurate final estimates could be obtained only much later in the outbreak. In particular, estimates of Tg were considerably less accurate in the bivariate case until the peak of incidence had passed.
The basic reproductive number and mean serial interval can be estimated simultaneously in real time during an outbreak of an emerging pathogen. Repeated application of these methods to small scale outbreaks at the start of an epidemic would permit accurate estimates of key parameters.
准确评估新型人类病原体的传播性和代间距是公共卫生的首要任务，以便确定干预措施的时机和所需力度。近期的理论工作聚焦于充分利用大群体中发病率初始指数增长阶段的数据。
我们通过基本再生数R0来衡量代际传播性，通过平均代间距Tg来衡量代间距。首先，我们为已知规模的小群体产生的病例数据构建了一个模拟算法，R0和Tg也已知。然后，我们开发了一个推理模型，用于计算这些病例数据出现的可能性，该可能性是R0和Tg的函数。该模型旨在捕捉：a）初始随机阶段代间距分布的任何信号；b）指数阶段的增长率；c）当小群体中的易感部分耗尽时，能产生特定峰值发病率形状的R0和Tg的独特组合。
广泛的重复模拟和参数估计表明，R0和Tg的单变量估计均无偏差。我们还能够同时估计R0和Tg。然而，只有在疫情爆发很久之后才能获得准确的最终估计值。特别是，在发病率峰值过去之前，双变量情况下Tg的估计准确性要低得多。
在新出现病原体的疫情爆发期间，可以实时同时估计基本再生数和平均代间距。在疫情开始时，将这些方法反复应用于小规模疫情，将能够准确估计关键参数。
