Innovations in Network Modeling for HIV Prevention Studies

HIV 预防研究网络建模的创新

• 批准号: 8659962
• 负责人: Mark Stephen Handcock
• 金额: $ 22.3万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-24 至 2015-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8659962
• 关键词: AIDS prevention; AIDS/HIV problem; Address; Attention; Censuses; Characteristics; Complex; Contact Tracing; County; Data; Data Collection; Dependency; Development; Epidemiology; Family; Graph; HIV/STD; Health; Human; Individual; Information Networks; Lead; Link; Los Angeles; Measurement; Measures; Methods; Modeling; Nodal; Outcome; Pathway Analysis; Population; Prevalence; Privacy; Probability Samples; Process; Property; Public Health; Recording of previous events; Respondent; Sampling; Simulate; Social Controls; Social Interaction; Social Network; Specific qualifier value; Statistical Models; Structure; Surveys; Work; base; behavioral/social science; design; improved; innovation; men who have sex with men; network models; novel; public health relevance; social; social model; theories; virtual

DESCRIPTION (provided by applicant): Statistical models for social networks have a long history in related public-health and the social and behavioral sciences. They can be used to provide precise stochastic representations of complex social structure, to compare theory to data and to simulate virtual networked populations that retain the essential properties of a theory or of data. This project will address fundamental issues in the statistical modeling of social networks and expands the existing capabilities. These are directly applicable to the epidemiological aspects of HIV/AIDS and STI both in the U.S. and internationally. Exponential-family random graph models are capable of representing the complex dependencies in social phenomena, and have been well studied in SNA. However, they do not represent the social endogeneity of nodal characteristics but only that of the relations. This project will address this deficiency by jointly stochastically modeling both the relational and individual variables via a novel class of exponential-family random network models. The majority of network data collection relies on sampling of the social network or is subject to missing data issues when a census is attempted. This project will develop new forms of network link- tracing designs that more efficiently collects information from the network while preserving the privacy of the networked population. Valid statistical inference from link-traced data is difficult because of th strong and often unknown dependencies in it. This project will develop a new framework for likelihood-based inference for social network models based on link-traced data when the covariates and outcome variables measured on the nodes are social endogenous. Many questions in health-related SNA are multivariate and can be stated as hypotheses about regressions of individual outcome variables on other covariates and their relational information. This project will extend network regression models to the more realistic situation where the outcomes, covariates and social relations are socially endogenous. The conceptual and methodological innovations will be applied to inferring HIV / STI prevalence among the IDU population in Los Angeles County and to HIV / STI among MSM in EU counties via the SIALON II project. The IDU data arise from an innovative link-tracing design to sample this hard-to- reach population. The social network structure of IDU will be inferred, and network regression will be used to analyze their HIV / STI prevalence. Privatized network sampling will be used in the MSN study.

描述（由申请人提供）：社交网络的统计模型在相关的公共卫生以及社会和行为科学中具有悠久的历史。它们可以用来提供复杂社会结构的精确随机表示，将理论与数据进行比较，并模拟保留理论或数据基本属性的虚拟网络人口。 该项目将解决社交网络统计建模中的基本问题，并扩展现有能力。这些都直接适用于艾滋病毒/艾滋病和性传播感染的流行病学方面在美国和国际。 指数族随机图模型能够描述社会现象中的复杂依赖关系，在SNA中得到了很好的研究。然而，它们并不代表节点特征的社会内涵，而只是关系的社会内涵。该项目将解决这一问题 通过一类新型指数族随机网络模型对关系变量和个体变量进行联合随机建模来弥补不足。 大多数网络数据收集依赖于对社交网络的采样，或者在尝试进行人口普查时会遇到数据缺失问题。该项目将开发新形式的网络链接跟踪设计，更有效地从网络收集信息，同时保护网络人口的隐私。 基于链接追踪数据的统计推断是一个困难的问题，因为它具有很强的依赖性，并且往往是未知的。本项目将开发一个新的框架，用于基于链接追踪数据的社会网络模型的基于似然性的推断，当节点上测量的协变量和结果变量是社会内生的。 健康相关SNA中的许多问题是多变量的，可以说是关于个体结果变量对其他协变量及其相关信息的回归的假设。该项目将网络回归模型扩展到更现实的情况下，结果，协变量和社会关系是社会内生的。 通过SIALON II项目，概念和方法创新将被应用于推断洛杉矶县注射吸毒人群中的艾滋病毒/性传播感染流行率和欧盟各县男男性行为者中的艾滋病毒/性传播感染流行率。注射吸毒者的数据来自一个创新的链接跟踪设计，以抽样这一难以接触的人口。将推断IDU的社会网络结构，并使用网络回归分析其艾滋病毒/性传播感染患病率。MSN研究将使用私有网络抽样。