Centre for Computational Evolution, University of Auckland, Auckland, New Zealand.
Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
Mol Biol Evol. 2019 Aug 1;36(8):1804-1816. doi: 10.1093/molbev/msz106.
Modern phylodynamic methods interpret an inferred phylogenetic tree as a partial transmission chain providing information about the dynamic process of transmission and removal (where removal may be due to recovery, death, or behavior change). Birth-death and coalescent processes have been introduced to model the stochastic dynamics of epidemic spread under common epidemiological models such as the SIS and SIR models and are successfully used to infer phylogenetic trees together with transmission (birth) and removal (death) rates. These methods either integrate analytically over past incidence and prevalence to infer rate parameters, and thus cannot explicitly infer past incidence or prevalence, or allow such inference only in the coalescent limit of large population size. Here, we introduce a particle filtering framework to explicitly infer prevalence and incidence trajectories along with phylogenies and epidemiological model parameters from genomic sequences and case count data in a manner consistent with the underlying birth-death model. After demonstrating the accuracy of this method on simulated data, we use it to assess the prevalence through time of the early 2014 Ebola outbreak in Sierra Leone.
现代系统发育动力学方法将推断出的系统发育树解释为部分传播链，提供有关传播和消除（消除可能是由于康复、死亡或行为改变）动态过程的信息。birth-death 和 coalescent 过程已被引入到模型中，以模拟 SIS 和 SIR 等常见流行病学模型下的流行病传播的随机动态，并且成功地与传播（出生）和消除（死亡）率一起用于推断系统发育树。这些方法要么通过对过去的发病率和患病率进行分析积分来推断率参数，因此不能明确推断过去的发病率或患病率，要么仅在大种群规模的合并极限下允许这种推断。在这里，我们引入了一种粒子滤波框架，以便从基因组序列和病例计数数据中以与基础 birth-death 模型一致的方式显式推断患病率和发病率轨迹以及系统发育和流行病学模型参数。在对模拟数据的准确性进行验证之后，我们使用它来评估塞拉利昂 2014 年埃博拉疫情早期的患病率。
