Statistical innovation to integrate sequences and phenotypes for scalable phylodynamic inference

项目总结/摘要 本提案针对贝叶斯统计方法和软件的设计、开发和分发 研究历史和快速演变的病原体的实时出现，如埃博拉病毒，人类免疫， 病毒，包括Lassa病毒、SARS-CoV-2病毒、西尼罗河病毒、黄热病病毒和寨卡病毒。该提案利用了 新颖的可扩展数据集成，使我们能够应对大规模流行病和流行病，并帮助采取行动- 健全的公共卫生政策。我们的多学科团队拥有统计思维，数据科学， 利用先进的测序技术和高通量技术， 生物实验，可以表征1000个病原体基因组，表型测量，生态， 从一次爆发中获得的逻辑和临床信息。我们的主要创新有三个方面。首先，我们将发明 并实施可扩展的贝叶斯贝叶斯动态技术，以整合表型测量和研究 它们与疾病传播相关的进化。其次，我们将培育生物丰富的进化模型， 通过新的有效算法绘制和理解疾病演变中的异质性。第三，我们将发展 高维和混合型表型模型，以使用 大规模并行计算虽然没有竞争软件存在整合表型和序列数据 在这个尺度上，我们将把我们的模型的限制情况与当前最先进的数据集进行比较 方法来评估计算性能的改善和偏见，这些限制注入使用真实的- 世界榜样。该提案将提供低级工具箱库和用户友好的部署软件 在统计学和医学中迅速扩大的大规模问题的范围。

PROJECT SUMMARY/ABSTRACT This proposal targets the design, development and distribution of Bayesian statistical methods and software to study the historical and real-time emergence of rapidly evolving pathogens, such as Ebola, human immun- odeficiency, influenza, Lassa, SARS-CoV-2, West Nile, yellow fever and Zika viruses. The proposal exploits novel scalable data integration to equip us for large-scale epidemics and pandemics and help inform action- able public health policy. Our multidisciplinary team carries expertise across statistical thinking, data science, evolutionary biology and infectious diseases to leverage advancing sequencing technology and high-throughput biological experimentation that can characterize 1000s of pathogen genomes, phenotype measurements, eco- logical and clinical information from a single outbreak. Our chief innovations are three-fold. First, we will invent and implement scalable Bayesian phylodynamic techniques to integrate phenotypic measurements and study their correlated evolution with disease spread. Second, we will foster biologically-rich evolutionary models to map and understand heterogeneity in disease evolution through new efficient algorithms. Third, we will develop high-dimensional and mixed-type phenotype models to link concerted viral genotype / phenotype changes using massively parallel computing. Although no competing software exists to integrate phenotype and sequence data at this scale, we will compare restricted cases of our models with reduced datasets to current state-of-the-art approaches to evaluate computational performance improvement and bias that these limitations inject using real- world examples. This proposal will deliver low-level toolbox libraries and user-friendly software for deployment across a rapidly expanding range of large-scale problems in statistics and medicine.