Models and Tools for Dynamic Health-Relevant Diffusion over Complex Networks

复杂网络上动态健康相关扩散的模型和工具

• 批准号: 8469203
• 负责人: James Moody
• 金额: $ 37.22万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8469203
• 关键词: Affect; Area; Behavior; Behavior Therapy; Behavioral; Belief; Communicable Diseases; Communities; Community Networks; Community Relations; Complex; Computer software; Data; Detection; Diet; Dietary Practices; Diffuse; Diffusion; Eating; Exercise; Foundations; Goals; HIV; Health; Health behavior; Healthcare; Human Papillomavirus; Individual; Intervention; Knowledge; Link; Mathematics; Methods; Modeling; Outcome; Performance; Plant Roots; Problem Solving; Process; Public Health; Research; Research Personnel; Risk; Risk Behaviors; Role; Shapes; Slice; Smoking; Social Behavior; Social Network; Solutions; Specificity; Structure; System; Techniques; Testing; Time; To specify; Translations; Vaccines; Virus; Work; base; design; disease transmission; health disparity; improved; insight; interest; member; novel; pathogen; programs; public health relevance; simulation; social; theories; tool; transmission process

DESCRIPTION (provided by applicant): Health and health behavior depend crucially on diffusion processes over social networks; both the spread of communicable diseases, such as HIV, and the diffusion of beliefs and practices that shape health behavior, such as dietary risk behavior, can be understood as generalized diffusion over (sometimes very complex and dynamic) social networks. However, despite the clear need, few of our formal analytic tools can be used to model diffusion over any but the simplest of these systems, as the mathematics for modeling diffusion over dynamic multi- relational networks are currently intractable except by direct simulation; which is often a weak foundation for generalizable knowledge; we provide a novel solution to modeling complex diffusion processes over dynamic networks by decomposing the networks into a set of tractable subsets and relation specific transmission rules. We will develop improved methods for network community detection that specifically targets understanding diffusion, and include such communities in our general framework; then test these new models against a bank of constructed and real social networks to assess model performance. Using simulation, novel new network clustering techniques and extensions of formal diffusion models, this project will develop the tools necessary to interpolate between the simplest direct diffusion cases and the most complex social behavior cases; advances will provide fundamental insights into health- relevant diffusion, yielding more robust modeling and highlighting directions for possible intervention.

描述（由申请人提供）：健康和健康行为在很大程度上取决于社会网络的扩散过程；传染病（如艾滋病毒）的传播，以及影响健康行为（如饮食风险行为）的信念和做法的传播，都可以理解为（有时非常复杂和动态的）社会网络的普遍传播。然而，尽管有明确的需求，我们的形式化分析工具很少可以用来模拟这些系统中最简单的扩散，因为在动态多关系网络上建模扩散的数学目前很难，除非通过直接模拟；这通常是泛化知识的薄弱基础；我们通过将网络分解为一组可处理的子集和特定于关系的传输规则，为动态网络上复杂扩散过程的建模提供了一种新的解决方案。我们将开发改进的网络社区检测方法，专门针对理解扩散，并将此类社区纳入我们的总体框架；然后在构建的和真实的社会网络中测试这些新模型，以评估模型的性能。利用模拟、新颖的网络聚类技术和正式扩散模型的扩展，该项目将开发必要的工具，在最简单的直接扩散案例和最复杂的社会行为案例之间进行插值；这些进展将为健康相关的扩散提供基本见解，产生更稳健的模型，并为可能的干预指明方向。