Advancing the Molecular Epidemiology of Infectious Diseases through Bayesian Phylogenetics

该项目旨在设计、开发和分发贝叶斯统计算法和软件，用于分析大量序列和表型数据，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，并将发明和实施基因型和表型进化的数据集成技术，以研究宿主和病原体之间的抗原竞争等问题。最后，该项目将开发高性能的统计工具，从分子流行病学的大数据中学习。统计计算技术将包括在基因组尺度上对现有参数模型的大规模并行化扩展和原始的非参数推理工具。与病原体传播及其相关疾病负担作斗争是一项巨大的挑战，需要持续的研究努力和果断的公共卫生措施，而基因组数据的可用性是表征这些病原体的重要资产。仍然缺乏的是统计思维和进化生物学的结合，通过系统发育重建与地理采样信息、病原体表型和流行病学动态来整合这些数据。作为统计系统发育中最紧迫的问题之一，本项目填补了这一空白。统计方面的进展将通过及时升级目前流行的软件和新的低级库来广泛传播。该项目的大规模并行算法和由此产生的软件将使它们能够在统计学和医学领域迅速扩大的大规模问题中得到部署。研究结果还将以可访问的、同行评议的文章形式提供，这些文章将参考基因组学以及更普遍的医学领域的其他大数据问题，描述这些进展。最后，这项研究还将为来自科学领域代表性不足群体的研究生、本科生和高中生提供培训。

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.