Statistical innovation to integrate sequences and phenotypes for scalable phylodynamic inference

统计创新整合序列和表型以进行可扩展的系统动力学推断

• 批准号: 10584588
• 负责人: Marc A. Suchard
• 金额: $ 45.9万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-09 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10584588
• 关键词: 2019-nCoV; Adoption; Africa; Algorithms; Applications Grants; Attention; Bayesian Method; Biological; Biology; Clinical; Clinical Management; Communicable Diseases; Communities; Computer software; Data; Data Science; Data Set; Development; Disease; Disease Outbreaks; Ebola; Ebola virus; Epidemic; Evolution; Factor Analysis; Fever; Fostering; Genomics; Genotype; HIV; Health Policy; Heterogeneity; Human Resources; Individual; Infectious Disease Epidemiology; Influenza; International; Intervention; Joints; Lassa virus; Libraries; Link; Manufacturer; Maps; Marriage; Measurement; Measures; Medicine; Methods; Modeling; Molecular Epidemiology; Peer Review; Performance; Phenotype; Phylogenetic Analysis; Productivity; Public Health; Publishing; Research; Sampling; Sampling Errors; Science; Scientist; Statistical Computing; Statistical Models; Suggestion; Techniques; Technology; Thinking; Time; Touch sensation; Training; Transportation; Trees; Underrepresented Minority; Underrepresented Populations; Viral; Viral Genome; West Nile; West Nile virus; Woman; Work; Yellow fever virus; Zika Virus; burden of illness; cohort; combat; comparative; computerized tools; data integration; data reduction; data streams; design; disability; genome sequencing; graduate student; high dimensionality; improved; influenzavirus; innovation; invention; minority scientist; multidisciplinary; next generation; novel; pandemic disease; parallel computer; pathogen; pathogen genome; pathogenic bacteria; phenotypic data; reconstruction; statistics; theories; trait; transmission process; undergraduate student; user friendly software

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.

项目摘要/摘要 该建议针对贝叶斯统计方法和软件的设计，开发和分布 研究快速发展的病原体的历史和实时出现，例如埃博拉病毒，人类免疫 效率，流体，Lassa，Sars-Cov-2，西尼罗河，黄热病和Zika病毒。提案利用 新颖的可扩展数据集成，使我们能够进行大规模的流行病和流行病，并有助于为行动提供信息 - 我们可以公共卫生政策。我们的多学科团队在统计思维，数据科学， 进化生物学和传染病来利用前进的测序技术和高通量 可以表征1000秒病原体基因组，表型测量，生态学的生物学实验 一次暴发的逻辑和临床信息。我们的主要创新是三倍。首先，我们将发明 并实施可扩展的贝叶斯系统动力学技术来整合表型测量并研究 它们与疾病扩散相关的演变。其次，我们将促进生物学丰富的进化模型 通过新的有效算法来了解疾病进化的异质性。第三，我们将发展 高维和混合型表型模型使用使用协同的病毒基因型 /表型链接使用 大量并联计算。尽管没有竞争软件来整合表型和序列数据 在此规模上，我们将将模型的限制案例与简化数据集进行比较与当前的最新情况 评估计算绩效改善和偏见的方法，这些限制使用REAL-注入 世界例子。该建议将提供低级工具箱库和用户友好的软件进行部署 在统计和医学方面的大规模大规模问题中迅速扩展。