Coalescent inference for infectious disease: meta-analysis of hepatitis C
Coalescent inference for infectious disease: meta-analysis of hepatitis C
复制标题
DOI：
10.1098/rstb.2012.0314
复制
发表时间：
2013-03-19
影响因子：
6.3
通讯作者：
Wilson, Daniel J.
中科院分区：
文献类型：
--
作者：
Dearlove, Bethany;Wilson, Daniel J.
病原基因组遗传分析是研究传染病种群动态和流行史的有力手段。然而，最广泛使用的凝聚方法的理论基础受到质疑，对它们的解释产生了怀疑。本研究的目的是为流行病学的区室模型建立可靠的种群遗传推断。利用基于元种群理论的一般方法，我们推导了易感-感染（SI）、易感-感染-易感（SIS）和易感-感染-恢复（SIR）动力学下的聚结模型。我们表明，当共感染可以忽略不计时，指数和逻辑增长模型分别等效于SI和SIS模型。在BEAST中实施SI、SIS和SIR模型，我们对丙型肝炎流行进行了荟萃分析，结果表明我们可以直接估计SIR动态下的基本繁殖数（R-0）和流行率。我们发现，流行病之间遗传多样性的差异可以通过潜在流行病学（流行病的年龄和当地人口密度）和病毒亚型的差异来解释。模型比较揭示了三种全球受限流行病的SIR动力学，但大多数更适合于更简单的SI动力学。综上所述，元种群模型为整合流行病学和种群遗传学以进行联合推断提供了一个通用和实用的框架。
Genetic analysis of pathogen genomes is a powerful approach to investigating the population dynamics and epidemic history of infectious diseases. However, the theoretical underpinnings of the most widely used, coalescent methods have been questioned, casting doubt on their interpretation. The aim of this study is to develop robust population genetic inference for compartmental models in epidemiology. Using a general approach based on the theory of metapopulations, we derive coalescent models under susceptible-infectious (SI), susceptible-infectious-susceptible (SIS) and susceptible-infectious-recovered (SIR) dynamics. We show that exponential and logistic growth models are equivalent to SI and SIS models, respectively, when co-infection is negligible. Implementing SI, SIS and SIR models in BEAST, we conduct a meta-analysis of hepatitis C epidemics, and show that we can directly estimate the basic reproductive number (R-0) and prevalence under SIR dynamics. We find that differences in genetic diversity between epidemics can be explained by differences in underlying epidemiology (age of the epidemic and local population density) and viral subtype. Model comparison reveals SIR dynamics in three globally restricted epidemics, but most are better fit by the simpler SI dynamics. In summary, metapopulation models provide a general and practical framework for integrating epidemiology and population genetics for the purposes of joint inference.