Novel Statistical Models for Synthesizing Social Networks and Epidemic Dynamics

综合社交网络和流行病动态的新颖统计模型

基本信息

批准号：
8118242
负责人：
David Hunter
金额：
$ 28.05万
依托单位：
PENNSYLVANIA STATE UNIVERSITY, THE
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-08-01 至 2013-07-31
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): Epidemic dynamics are key in basic and applied population biology, and the application of social network models to the spread of infectious diseases is an intuitive refinement to the classical assumption of population-wide random mixing. However, there is currently a disjunction between existing mathematical models for contact networks, which underlie epidemic dynamics, and real-world network data. The proposed project will address this gap by developing a new synthesis of recently developed statistical methods. In most systems, although observed data on the true contact network are unavailable, ancillary data (host and pathogen genetic data and coarse-grain data on social and spatial relationships) exist that provide information about the contact network. Yet there are formidable mathematical challenges in developing probability models for contact networks using data that do not include an actual observed network. Even if such a model were obtained, conducting predictive inference on epidemic dynamics requires a simulation framework that will generate unbiased realizations from the network model on which we can test epidemic and evolutionary processes. This project will develop methods to address both of these challenges. Our specific goals and associated approaches are: (1) To improve statistical methods for estimating transmission and contact networks from diverse biological data, based on a novel integration of dual infection and selection graph (DISG) and exponential random graph model (ERGM) approaches. (2) To generalize from a particular realization of a transmission network process to a probabilistic model for the underlying contact-generating process. This phase will be based on refinements of ERGMs to allow for incomplete network data, as estimated in Goal 1. (3) To validate, refine, and generalize the models developed in Goals 1 and 2 using recursive simulation methods. (4) To ground-truth the methods using uniquely detailed data on a host/pathogen system involving a fast-evolving virus (feline immunodeficiency virus, FIV) in wild cougars. Our research will contribute importantly to human health through novel understanding of disease transmission in populations with unique contact and movement patterns, thereby informing public health officials of epidemic risks and potential intervention strategies for both well-characterized and novel infections.

描述（由申请人提供）：流行动力学是基本和应用人群生物学的关键，而社交网络模型在传染病的传播中的应用是对整个人口随机混合的经典假设的直观改进。但是，目前在接触网络的现有数学模型（基于流行病动力学的基础）和现实世界网络数据的现有数学模型之间存在脱节。拟议的项目将通过开发最近开发的统计方法的新综合来解决这一差距。在大多数系统中，尽管在真实接触网络上观察到的数据是不可用的，但仍存在有关辅助数据（主机和病原体遗传数据以及有关社交和空间关系的粗粒数据），这些数据提供了有关接触网络的信息。然而，使用不包含实际观察到的网络的数据为触点网络开发概率模型时存在着巨大的数学挑战。即使获得了这样的模型，对流行动力学进行预测推断也需要一个模拟框架，该框架将从网络模型中产生无偏见的实现，我们可以测试流行病和进化过程。该项目将开发解决这两个挑战的方法。我们的具体目标和相关方法是：（1）基于双重感染和选择图（DISG）和指数随机图模型（ERGM）方法的新型集成，改善了从不同生物学数据估算传输和接触网络的统计方法。（2）从特定实现传输网络过程的特定实现到基础接触生成过程的概率模型。此阶段将基于ERGM的改进，以允许使用目标1中的目标1中的不完整网络数据。（3）使用递归模拟方法在目标1和2中验证，完善和概括模型。（4）使用涉及野生美洲狮的宿主/病原体系统（猫免疫缺陷病毒，FIV）的宿主/病原体系统的唯一详细数据基础。我们的研究将通过对具有独特接触和运动模式的人群中的疾病传播的新知识来为人类健康做出重要贡献，从而向公共卫生官员告知流行病风险和潜在的干预策略，以供良好的特征和新型感染。