Spatial modeling of Mycobacterium tuberculosis transmission with dyadic genetic relatedness data.
Spatial modeling of Mycobacterium tuberculosis transmission with dyadic genetic relatedness data.
复制标题
使用二元遗传相关性数据对结核分枝杆菌传播进行空间建模。
作者：
Warren,JoshuaL;Chitwood,MelanieH;Sobkowiak,Benjamin;Colijn,Caroline;Cohen,Ted
了解导致两个人之间病原体传播可能性增加的因素对于感染控制很重要。然而，分析病原体相关性的测量来估计这些相关性是复杂的，因为由于同一个体存在于多个并向量结果中的相关性，由于未测量的传播动力学引起的潜在的空间相关性，以及一些结果的独特的分布特征。我们开发了两种新的分层贝叶斯空间方法来分析二元病原体遗传关联性数据，以Patristic距离和传播概率的形式，同时解决了这些并发症。使用个体水平的空间相关随机效应参数，我们解释了结果之间的多个相关来源及其分布的其他重要特征。通过仿真，我们展示了现有方法在估计感兴趣的关键关联方面的局限性，以及新方法在具有不同级别相关性的数据集上纠正这些问题的能力。所有方法都适用于摩尔多瓦共和国的结核分枝杆菌数据，我们在那里确定了与疾病传播相关的以前未知的因素，并通过分析随机效应参数，确定了关键个人和传播活动增加的地区。模型比较显示了新方法在这种背景下的重要性。这些方法在R包GenePair中实现。
Understanding factors that contribute to the increased likelihood of pathogen transmission between two individuals is important for infection control. However, analyzing measures of pathogen relatedness to estimate these associations is complicated due to correlation arising from the presence of the same individual across multiple dyadic outcomes, potential spatial correlation caused by unmeasured transmission dynamics, and the distinctive distributional characteristics of some of the outcomes. We develop two novel hierarchical Bayesian spatial methods for analyzing dyadic pathogen genetic relatedness data, in the form of patristic distances and transmission probabilities, that simultaneously address each of these complications. Using individual-level spatially correlated random effect parameters, we account for multiple sources of correlation between the outcomes as well as other important features of their distribution. Through simulation, we show the limitations of existing approaches in terms of estimating key associations of interest, and the ability of the new methodology to correct for these issues across datasets with different levels of correlation. All methods are applied toMycobacterium tuberculosisdata from the Republic of Moldova, where we identify previously unknown factors associated with disease transmission and, through analysis of the random effect parameters, key individuals, and areas with increased transmission activity. Model comparisons show the importance of the new methodology in this setting. The methods are implemented in the R packageGenePair.