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

基本信息

批准号：
10390334
负责人：
Marc A. Suchard
金额：
$ 46.59万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
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/10390334
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 Epidemic Evolution Factor Analysis Fever Fostering Genomics Genotype HIV Health Policy Heterogeneity Human Human Resources Individual Infectious Disease Epidemiology Influenza International Intervention Joints Libraries Link Manufacturer Name Maps Marriage Measurement Measures Medicine Methods Modeling Molecular Epidemiology Peer Review Performance Phenotype Phylogenetic Analysis 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 virus Woman Work Yellow Fever Zika Virus burden of illness cohort combat comparative computerized tools data integration data streams design disability genome sequencing graduate student high dimensionality innovation 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

项目摘要

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.

项目概要/摘要该提案针对贝叶斯统计方法和软件的设计、开发和分发研究快速进化的病原体的历史和实时出现，例如埃博拉、人类免疫该提案利用了感染、流感、拉沙、SARS-CoV-2、西尼罗河、黄热病和寨卡病毒。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，并帮助为行动提供信息我们的多学科团队拥有统计思维、数据科学、进化生物学和传染病利用先进的测序技术和高通量生物实验可以表征数千种病原体基因组、表型测量、生态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系统动力学技术来整合表型测量和研究其次，我们将建立丰富的生物学进化模型来研究它们与疾病传播的相关性。通过新的高效算法来绘制和理解疾病进化的异质性。高维和混合型表型模型，使用以下方法将协调一致的病毒基因型/表型变化联系起来大规模并行计算。尽管没有竞争软件可以集成表型和序列数据。在这个规模上，我们将使用减少的数据集将模型的受限情况与当前最先进的技术进行比较评估计算性能改进和这些限制注入的偏差的方法使用真实的该提案将提供低级工具箱库和用户友好的部署软件。涵盖统计和医学领域迅速扩大的大规模问题。