Real-time characterization of the molecular epidemiology of an influenza pandemic

Early characterization of the epidemiology and evolution of a pandemic is essential for determining the most appropriate interventions. During the 2009 H1N1 influenza A pandemic, public databases facilitated widespread sharing of genetic sequence data from the outset. We employ Bayesian phylogenetics to simulate real-time estimation of the evolutionary rate, date of emergence and intrinsic growth rate (r0) of the pandemic from whole-genome sequences. We investigate the effects of temporal range of sampling and dataset size on the precision and accuracy of parameter estimation. Parameters can be accurately estimated as early as two months after the first reported case, from 100 genomes. Early deleterious mutations were purged from the population during the second pandemic wave and the choice of growth model is important for accurate estimation of r0. This demonstrates the utility of simple coalescent models to rapidly inform intervention strategies during a pandemic.对大流行病的流行病学特征与演化过程进行早期解析，对于确定最恰当的干预措施至关重要。在2009年甲型H1N1流感大流行期间，公共数据库自疫情伊始便为遗传序列数据的广泛共享提供了便利。我们采用贝叶斯系统发育学（Bayesian phylogenetics）方法，基于全基因组序列，模拟开展针对该大流行演化速率、出现日期以及内在生长速率（r0）的实时估算。我们探究了采样时间跨度与数据集规模对参数估算精度与准确性的影响。仅需100条全基因组序列，最早可在首例报告病例出现后的两个月内完成参数的准确估算。在大流行的第二波传播阶段，早期有害突变已从种群中被清除；且生长模型的选择对于r0的准确估算至关重要。本研究表明，简单溯祖模型（coalescent models）可在大流行期间快速为干预策略的制定提供科学参考。