A Systematic Bayesian Integration of Epidemiological and Genetic Data.
A Systematic Bayesian Integration of Epidemiological and Genetic Data.
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DOI：
10.1371/journal.pcbi.1004633
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发表时间：
2015-11
影响因子：
4.3
通讯作者：
Gibson G
中科院分区：
文献类型：
--
作者：
Lau MS;Marion G;Streftaris G;Gibson G
关于病原体的遗传序列数据具有巨大的潜力，可以为推断其传播动态提供信息，最终导致更好的疾病控制。如果遗传变化和疾病传播发生在可比较的时间尺度上，可以通过联合分析这种遗传序列数据和基于临床症状和诊断测试的流行病学观察来推断额外的信息。尽管最近引入的方法代表了实质性的进展，但由于计算原因，它们近似于对病原体群体中的疾病动力学和遗传变化的真正联合推断，部分捕捉到了联合流行病学-进化动力学。需要改进方法，将这些遗传数据与流行病学观察充分结合起来，以便更有力地推断传播树和其他关键流行病学参数，如潜伏期。这里，在现有文献的基础上，提出了一种新的贝叶斯框架，该框架可以同时显式地推断传播树和未观察到的传播病原体序列。我们的框架便于使用现实的似然函数，并能够根据部分观察到的疫情对流行病学-进化过程进行系统和真正的联合推断。使用模拟数据表明，这种方法能够准确地推断联合流行病学-进化动力学，即使在病原体序列和流行病学数据不完整的情况下，当只有一小部分暴露的序列可用时也是如此。这些结果还表征和量化了不完全和部分序列数据的价值，这对抽样设计具有重要意义，并证明了所介绍的方法在疫情中识别多个聚集性的能力。该框架被用于分析英国的一次口蹄疫暴发，增强了目前对其传播动力学和进化过程的了解。在越来越多的病原体序列数据中，一个关键的挑战是将这些数据与传统的流行病学数据更好地结合起来，其最近的目标是可靠的预测，最终目标是有效地管理疾病暴发。虽然这种整合已经取得了实质性的进展，并且它们提高了我们对许多疾病动力学的理解，但目前的方法依赖于快速算法，而不是实现对联合流行病学-进化过程的系统整合和准确推断。在现有文献方法的基础上，本文描述了一种新的贝叶斯方法，用于系统地整合这两个数据流。我们提出了一种计算上易于处理的贝叶斯推理算法，该算法考虑了整个联合流行病学-进化过程。利用该算法，我们系统地研究了遗传数据的价值，为未来的抽样设计提供了有价值的见解。该算法随后被应用于描述英国动物口蹄疫传播的真实世界数据集，展示了与我们的方法实现这种系统集成的重要性。
Genetic sequence data on pathogens have great potential to inform inference of their transmission dynamics ultimately leading to better disease control. Where genetic change and disease transmission occur on comparable timescales additional information can be inferred via the joint analysis of such genetic sequence data and epidemiological observations based on clinical symptoms and diagnostic tests. Although recently introduced approaches represent substantial progress, for computational reasons they approximate genuine joint inference of disease dynamics and genetic change in the pathogen population, capturing partially the joint epidemiological-evolutionary dynamics. Improved methods are needed to fully integrate such genetic data with epidemiological observations, for achieving a more robust inference of the transmission tree and other key epidemiological parameters such as latent periods. Here, building on current literature, a novel Bayesian framework is proposed that infers simultaneously and explicitly the transmission tree and unobserved transmitted pathogen sequences. Our framework facilitates the use of realistic likelihood functions and enables systematic and genuine joint inference of the epidemiological-evolutionary process from partially observed outbreaks. Using simulated data it is shown that this approach is able to infer accurately joint epidemiological-evolutionary dynamics, even when pathogen sequences and epidemiological data are incomplete, and when sequences are available for only a fraction of exposures. These results also characterise and quantify the value of incomplete and partial sequence data, which has important implications for sampling design, and demonstrate the abilities of the introduced method to identify multiple clusters within an outbreak. The framework is used to analyse an outbreak of foot-and-mouth disease in the UK, enhancing current understanding of its transmission dynamics and evolutionary process. In the midst of increasingly available sequence data of pathogens, a key challenge is to better integrate these data with traditional epidemiological data, with the proximate goal of reliable prediction and the ultimate aim of effective management of disease outbreaks. Although substantial advances have been made for such an integration, and they have improved our understandings of many disease dynamics which are not available otherwise, current methods have relied on fast algorithms, rather than achieving a systematic integration and accurate inference of the joint epidemiological-evolutionary process. Building on methods in current literature, this paper describes a novel Bayesian approach for systematically integrating these two streams of data. We propose a computationally tractable Bayesian inferential algorithm which takes the full joint epidemiological-evolutionary process into account. Using this algorithm, we study systematically the value of genetic data, providing valuable insights into future sampling designs. The algorithm is subsequently applied to real-world dataset describing the spread of animal foot-and-mouth disease in the UK, demonstrating the importance of such a systematic integration achieved with our methodology.