Survival analysis and regression in infectious disease epidemiology

项目概要/摘要 该项目旨在为传染病开发更灵活、更强大的统计方法 基于生存分析和接触间隔的流行病学。与人的接触时间间隔 A对人B的传染性是从A开始传染到从A接触传染之间的时间 至B，其中传染性接触定义为足以感染易感者的接触。 如果B被A以外的人感染，或者A从 在与B感染性接触之前的传染性。由于这种删失，生存分析 估计接触间隔分布的自然方法。在K99阶段， 项目将开发接触间隔分布的非参数估计， 半参数回归模型允许估计代表 协变量对传染性和易感性的影响。由于这些估计中的许多可能是 表示为可能的传输树上的总和或平均值，它们提供了一个自然的 传统流行病数据和系统发育数据之间的桥梁。在R 00阶段， K99方法的推广将被开发并置于贝叶斯框架中， 对部分观察到的流行病的分析。生存分析是现代医学的支柱之一 生物统计学，所以它将是一个丰富的来源，新的研究设计和分析方法， 传染病流行病学

Project summary/abstract The project aims to develop more flexible and powerful statistical methods for infectious disease epidemiology based on survival analysis and contact intervals. The contact interval from person A to person B is the time between the onset of infectiousness in A and infectious contact from A to B, where infectious contact is defined to be a contact sufficient to infect a susceptible person. It can be right-censored if B is infected by someone other than A or if A recovers from infectiousness prior to infectious contact with B. Because of this censoring, survival analysis is the natural approach to estimating the contact interval distribution. During the K99 phase, this project will develop nonparametric estimates of the contact interval distribution and semiparametric regression models allowing the estimation of log hazard ratios representing the effects of covariates on infectiousness and susceptibility. Since many of these estimates can be represented as sums or averages over possible transmission trees, they provide a natural bridge between traditional epidemic data and phylogenetic data. During the R00 phase, generalizations of the K99 methods will be developed and placed into a Bayesian framework for the analysis of partially-observed epidemics. Survival analysis is one of the pillars of modern biostatistics, so it will be a rich source of novel study designs and analytical methods in infectious disease epidemiology.