Duvvuri Venkata R, Hicks Joseph T, Damodaran Lambodhar, Grunnill Martin, Braukmann Thomas, Wu Jianhong, Gubbay Jonathan B, Patel Samir N, Bahl Justin
Public Health Ontario, Toronto, Ontario, Canada.
Department of Laboratory Medicine and Pathobiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
Infect Dis Model. 2023 Feb 16;8(1):240-252. doi: 10.1016/j.idm.2023.02.003. eCollection 2023 Mar.
Technological advancements in phylodynamic modeling coupled with the accessibility of real-time pathogen genetic data are increasingly important for understanding the infectious disease transmission dynamics. In this study, we compare the transmission potentials of North American influenza A(H1N1)pdm09 derived from sequence data to that derived from surveillance data. The impact of the choice of tree-priors, informative epidemiological priors, and evolutionary parameters on the transmission potential estimation is evaluated. North American Influenza A(H1N1)pdm09 hemagglutinin (HA) gene sequences are analyzed using the coalescent and birth-death tree prior models to estimate the basic reproduction number ( ). Epidemiological priors gathered from published literature are used to simulate the birth-death skyline models. Path-sampling marginal likelihood estimation is conducted to assess model fit. A bibliographic search to gather surveillance-based values were consistently lower (mean ≤ 1.2) when estimated by coalescent models than by the birth-death models with informative priors on the duration of infectiousness (mean ≥ 1.3 to ≤2.88 days). The user-defined informative priors for use in the birth-death model shift the directionality of epidemiological and evolutionary parameters compared to non-informative estimates. While there was no certain impact of clock rate and tree height on the estimation, an opposite relationship was observed between coalescent and birth-death tree priors. There was no significant difference (p = 0.46) between the birth-death model and surveillance estimates. This study concludes that tree-prior methodological differences may have a substantial impact on the transmission potential estimation as well as the evolutionary parameters. The study also reports a consensus between the sequence-based estimation and surveillance-based estimates. Altogether, these outcomes shed light on the potential role of phylodynamic modeling to augment existing surveillance and epidemiological activities to better assess and respond to emerging infectious diseases.
系统发育动力学建模的技术进步，再加上实时病原体基因数据的可获取性，对于理解传染病传播动态变得越来越重要。在本研究中，我们比较了从序列数据得出的北美甲型H1N1pdm09流感的传播潜力与从监测数据得出的传播潜力。评估了树先验、信息性流行病学先验和进化参数的选择对传播潜力估计的影响。使用合并和生死树先验模型分析北美甲型H1N1pdm09血凝素（HA）基因序列，以估计基本再生数（ ）。从已发表文献中收集的流行病学先验用于模拟生死天际线模型。进行路径抽样边际似然估计以评估模型拟合。通过文献检索收集基于监测的 值，当通过合并模型估计时，其值始终低于（平均值≤1.2）通过对传染性持续时间具有信息性先验的生死模型估计的值（平均值≥1.3至≤2.88天）。与无信息估计相比，用于生死模型的用户定义信息性先验改变了流行病学和进化参数的方向性。虽然时钟速率和树高对 估计没有确定的影响，但在合并树先验和生死树先验之间观察到相反的关系。生死模型和监测 估计之间没有显著差异（p = 0.46）。本研究得出结论，树先验方法学差异可能对传播潜力估计以及进化参数产生重大影响。该研究还报告了基于序列的 估计与基于监测的 估计之间的一致性。总之，这些结果揭示了系统发育动力学建模在增强现有监测和流行病学活动以更好地评估和应对新兴传染病方面的潜在作用。
