Advancing the Molecular Epidemiology of Infectious Diseases through Bayesian Phylogenetics

该项目的目标是设计、开发和分发贝叶斯统计算法和软件，用于分析大量的序列和表型数据，以研究快速进化的病原体的出现和传播。为此，该项目将培育新的系统地理模型，以探索疾病传播和生态障碍的机制，并将发明和实施用于基因和表型进化的数据集成技术，以研究例如宿主和病原体之间的抗原竞争。最后，该项目将开发高性能的统计工具，以学习分子流行病学中的大数据。统计计算技术将包括在基因组水平上对现有参数模型进行大规模并行化扩展，以及原始的非参数推理工具。抗击病原体传播及其相关疾病负担是一个巨大的挑战，需要持续的研究努力和确定的公共卫生措施，而基因组数据的可用性为表征这些病原体提供了重要的资产。仍然缺乏的是统计学思维和进化生物学的结合，通过系统发育重建与地理抽样信息和病原体表型和流行病学动态整合这些数据。作为统计系统发育学中最紧迫的问题之一，该项目填补了这一空白。将通过及时升级目前流行的软件和新的低级图书馆来广泛传播统计方面的进展。这个项目的大规模并行算法和产生的软件将使它们能够在统计学和医学中迅速扩大的大规模问题范围内部署。结果还将以可访问的同行评议文章的形式提供，这些文章参考基因组学以及更广泛地说，涉及医学中的其他大数据问题来描述这些进展。最后，这项研究还将为研究生、本科生和高中生提供培训，这些学生来自科学界代表性不足的群体。

This project targets the design, development and distribution of Bayesian statistical algorithms and software for analysis of massive amounts of sequence and phenotype data to study the emergence and spread of rapidly evolving pathogens. To this end, the project will foster novel phylogeographic models to explore mechanisms of disease spread and ecological barriers, and will invent and implement data integration techniques for genotypic and phenotypic evolution to study, for example, antigenic competition between the host and pathogen. Finally, the project will develop high-performance statistical tools to learn from such Big Data in molecular epidemiology. Statistical computing techniques will include massive parallelization extensions for existing parametric models at the genome-scale and original non-parametric inference tools.Combating pathogen spread and their associated disease burden is a tremendous challenge requiring sustained research effort and decided public health measures, and the availability of genomic data provides a major asset in characterizing these pathogens. What remains lacking is a marriage of statistical thinking and evolutionary biology to integrate these data through phylogenetic reconstructions with geographic sampling information and pathogen phenotypic and epidemiological dynamics. As one of the most pressing problems in statistical phylogenetics, this project fills this gap. Statistical advances will be widely disseminated through timely upgrades to currently popular software and new low-level libraries. The massively parallel algorithms and resulting software from this project will enable their deployment across a rapidly expanding range of large-scale problems in statistics and medicine. Results will also be provided in accessible, peer-reviewed articles that describe these advances with reference to genomics and, more generally, to other Big Data problems in medicine. Finally, the research will also provide training to graduate, undergraduate, and high school students from groups underrepresented in the sciences.