Molecular Infectious Disease Epidemiology: Survival Analysis and Algorithms Linking Phylogenies to Transmission Trees.
Molecular Infectious Disease Epidemiology: Survival Analysis and Algorithms Linking Phylogenies to Transmission Trees.
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DOI：
10.1371/journal.pcbi.1004869
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发表时间：
2016-04
影响因子：
4.3
通讯作者：
Longini IM Jr
中科院分区：
文献类型：
--
作者：
Kenah E;Britton T;Halloran ME;Longini IM Jr
最近的工作试图利用病原体的全基因组序列数据来重建疾病暴发中连接感染者和被感染者的传播树。然而，一次疫情的传播树不能推广到未来的疫情。传播树的重建对公共卫生是最有用的，如果它导致关于疾病传播的普遍科学见解。在生存分析框架中，传输参数的估计是基于可能的传输树的总和或平均值。通过提供关于谁感染了谁的部分信息，遗传学可以提高这些估计的精确度。该菌的叶子代表了已采样的病原体，它们具有已知的宿主。内部节点代表采样病原体的共同祖先，这些病原体具有未知的宿主。从疾病生物学和流行病学研究设计的假设出发，我们证明了内部节点主机和传输树的可能分配之间存在一一对应关系，同时与人，地点和时间上的流行病学数据和流行病学数据一致。我们开发的算法来枚举这些传输树，并显示这些可以用来计算的可能性，将流行病学数据和一个遗传学。一项模拟研究证实，这导致更有效的估计传染性和基线危害的传染性接触的风险比，我们使用这些方法来分析2001年在英国口蹄疫病毒爆发的数据。这些结果表明，关于逃脱感染的个人的数据很重要，而这一点往往被忽视。生存分析和算法相结合，将遗传学与传播树联系起来，是分子传染病流行病学的严格但灵活的统计基础。最近的工作试图利用病原体的全基因组序列数据来重建疾病暴发中连接感染者和被感染者的传播树。然而，一次疫情的传播树不能推广到未来的疫情。传播树的重建对公共卫生是最有用的，如果它导致关于疾病传播的普遍科学见解。对传播参数的准确估计有助于确定传播的风险因素，并有助于设计和评估针对新出现感染的公共卫生干预措施。使用时间到事件数据的统计方法（生存分析），传输参数的估计是基于可能的传输树的总和或平均值。通过提供关于谁感染了谁的部分信息，病原体传播学可以减少可能的传播树的集合，并提高传播参数估计的精度。我们推导出的算法，枚举的传播树符合病原体的致病性和流行病学数据，显示如何计算的可能性与致病性的传播数据，并将这些方法应用于2001年在英国爆发的口蹄疫。这些方法将使病原体基因序列被纳入疫情调查、疫苗试验和其他传染病传播研究的分析中。
Recent work has attempted to use whole-genome sequence data from pathogens to reconstruct the transmission trees linking infectors and infectees in outbreaks. However, transmission trees from one outbreak do not generalize to future outbreaks. Reconstruction of transmission trees is most useful to public health if it leads to generalizable scientific insights about disease transmission. In a survival analysis framework, estimation of transmission parameters is based on sums or averages over the possible transmission trees. A phylogeny can increase the precision of these estimates by providing partial information about who infected whom. The leaves of the phylogeny represent sampled pathogens, which have known hosts. The interior nodes represent common ancestors of sampled pathogens, which have unknown hosts. Starting from assumptions about disease biology and epidemiologic study design, we prove that there is a one-to-one correspondence between the possible assignments of interior node hosts and the transmission trees simultaneously consistent with the phylogeny and the epidemiologic data on person, place, and time. We develop algorithms to enumerate these transmission trees and show these can be used to calculate likelihoods that incorporate both epidemiologic data and a phylogeny. A simulation study confirms that this leads to more efficient estimates of hazard ratios for infectiousness and baseline hazards of infectious contact, and we use these methods to analyze data from a foot-and-mouth disease virus outbreak in the United Kingdom in 2001. These results demonstrate the importance of data on individuals who escape infection, which is often overlooked. The combination of survival analysis and algorithms linking phylogenies to transmission trees is a rigorous but flexible statistical foundation for molecular infectious disease epidemiology. Recent work has attempted to use whole-genome sequence data from pathogens to reconstruct the transmission trees linking infectors and infectees in outbreaks. However, transmission trees from one outbreak do not generalize to future outbreaks. Reconstruction of transmission trees is most useful to public health if it leads to generalizable scientific insights about disease transmission. Accurate estimates of transmission parameters can help identify risk factors for transmission and aid the design and evaluation of public health interventions for emerging infections. Using statistical methods for time-to-event data (survival analysis), estimation of transmission parameters is based on sums or averages over the possible transmission trees. By providing partial information about who infected whom, a pathogen phylogeny can reduce the set of possible transmission trees and increase the precision of transmission parameter estimates. We derive algorithms that enumerate the transmission trees consistent with a pathogen phylogeny and epidemiologic data, show how to calculate likelihoods for transmission data with a phylogeny, and apply these methods to a foot and mouth disease outbreak in the United Kingdom in 2001. These methods will allow pathogen genetic sequences to be incorporated into the analysis of outbreak investigations, vaccine trials, and other studies of infectious disease transmission.