Understanding Transmission with Integrated Genetic and Epidemiologic Inference

通过综合遗传和流行病学推断了解传播

• 批准号: 9307943
• 负责人: Trevor BC Bedford
• 金额: $ 119.11万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9307943
• 关键词: Case Series; Case Study; Communicable Diseases; Data; Data Set; Dengue; Disease Outbreaks; Epidemiology; Foundations; Genealogy; Genetic; Goals; Heterogeneity; Household; Individual; Influenza; Influenza A Virus, H7N9 Subtype; Joints; Knowledge; Learning; Markov Chains; Methodology; Methods; Modeling; Pattern; Persons; Population; Population Dynamics; Risk Factors; Sampling; Schools; Series; Severe Acute Respiratory Syndrome; Structure; Time; Trees; Viral; Virus; Work; epidemiologic data; epidemiological model; genetic approach; improved; life history; novel; particle; pathogen; public health relevance; statistics; theories; tool; transmission process; virus genetics

The overall goal of this project is to develop and validate novel methods to perform joint inference from combined epidemiologic and genetic data. This inference methodology seeks to provide estimates of fundamental transmission parameters, such as RO, as well as provide estimates of unobserved transmission trees and unobserved counts of susceptible, infected and recovered individuals in the population through time. We focus on two common scenarios. In the first, we target densely sampled, but localized, epidemiologic and genetic data, in which the person, place and time are known, and in which pathogen genetic samples are obtained. These sorts of datasets are commonly generated during transmission studies in households, schools, and similar settings, but also in analyses of novel outbreaks such as SARS or H7N9. Our inference framework seeks to estimate host-to-host transmission networks from combined epidemiologic and genetic data. In the second scenario, we target sparsely sampled, but broader in scope, epidemiologic and genetic data, in which we observe a time series of case reports and sparsely sampled pathogen genetic sequences. In this inference framework, we seek to model population-level transmission processes from a relatively small samples of cases. This framework utilizes coalescent theory to extrapolate from sampled genetic sequences to population-level dynamics. In implementation, we plan to utilize sophisticated inference methodology that combines Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) approaches in what's termed particle MCMC (PMCMC). We plan to utilize these novel inference methods to investigate transmission heterogeneity and local transmission structure in influenza, phenomena that have been difficult to fully analyze without a combined epidemiologic and genetic inference framework in place.

这个项目的总体目标是开发和验证执行联合推理的新方法 综合流行病学和遗传学数据。这种推论方法试图提供对 基本传输参数，如RO，以及提供未观察到的传输的估计 树木和种群中未观察到的易感、感染和康复个体的数量 时间到了。我们关注两个常见的场景。在第一个阶段，我们的目标是密集采样的，但定位的， 流行病学和遗传学数据，其中人、地点和时间是已知的，病原体是 获得了遗传样本。这些类型的数据集通常在传播研究期间生成 不仅在家庭、学校和类似环境中，而且在对SARS或H7N9等新型疫情的分析中也是如此。 我们的推断框架试图从综合流行病学中估计宿主到宿主的传播网络 和基因数据。在第二种情况下，我们的目标是抽样稀少但范围更广的流行病学 和遗传数据，在这些数据中，我们观察到病例报告的时间序列和稀疏采样的病原体基因 序列。在这个推论框架中，我们试图从一个 病例样本相对较少。该框架利用合并理论从样本中进行外推 从遗传序列到种群水平的动态。在实现中，我们计划使用复杂的推理 马尔可夫链蒙特卡罗(MCMC)和序贯蒙特卡罗(SMC)相结合的方法 在所谓的粒子MCMC(PMCMC)中。我们计划利用这些新的推理方法来 调查流感的传播异质性和局部传播结构，这些现象具有 如果没有一个综合的流行病学和遗传推断框架，就很难进行充分的分析。