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ConProject-001

基本信息

批准号：
9767186
负责人：
Alexander Volfovsky
金额：
$ 27.33万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
资助国家：
美国
起止时间：
至 2021-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9767186
关键词：
Address Affect Algorithms Behavior Behavioral Big Data Biological Biological Process Biometry Categories Characteristics Clinical Communicable Diseases Complex Controlled Clinical Trials Data Data Analyses Data Set Databases Development Dimensions Disease Disease Outbreaks Effectiveness of Interventions Evaluation Friendships Goals Individual Influenza Information Networks Intervention Lead Left Literature Location Measures Methodology Methods Mining Modeling Modernization Observational Study Policies Population Property Public Health Randomized Recommendation Reporting Research Running Social Network Structure Students Surveys Symptoms Techniques Time authority base college computerized tools contagion disease transmission efficacy evaluation experimental study flu insight pandemic disease preservation prevent psychologic public health intervention statistics tool

项目摘要

It is well known that causal analysis frequently suffers when relevant variables are left unobserved. Because of this, many modern public health datasets have started including massive quantities of previously unavailable information on each individual. For example, a recent study of flu-like-illness spread on college campuses has collected numerous different static and dynamic networks, biometric information, as well as standard demographic data for each individual. This project develops new statistical and computational tools that incorporate these new data structures into the evaluation of different interventions and produce interpretable causal analyses. Typical approaches to such causal analyses rely on strong modeling assumptions and dimension-reduction techniques that throw away relevant information about individuals and can lead to biased causal estimates. For example, when network information is collected it is frequently reduced to egocentric summaries that do not reflect the overall network structure. The goals of this project are as follows: 1. Develop fast almost-matching-exactly algorithms that construct matched sets for causal inference in massive datasets. 2. Develop methods for matching on available network information in order to better understand how biological processes spread. These tools are widely applicable and may lead to new insights into complex causal mechanisms. In particular this study will evaluate the efficacy of isolation interventions on flu-like-illness spread and propose new and efficient interventions to battle pandemic spread.

众所周知，当相关变量没有被观察到时，因果分析经常会受到影响。因为其中，许多现代公共卫生数据集已经开始包括大量以前的无法获得每个人的信息。例如，最近一项关于流感样疾病在大学中传播的研究校园已经收集了大量不同的静态和动态网络、生物识别信息以及每个人的标准人口统计数据。该项目开发了新的统计和计算工具将这些新的数据结构纳入对不同干预措施的评价，并产生可解释的因果分析。这种因果分析的典型方法依赖于强大的建模假设和降维技术，丢弃有关个人和可能会导致有偏见的因果估计。例如，当收集网络信息时，它是频繁的简化为以自我为中心的总结，不能反映整体网络结构。该项目的目标如下： 1.提出了一种快速的几乎完全匹配算法，该算法可以构造用于因果推理的匹配集海量数据集。 2.制定匹配可用网络信息的方法，以便更好地了解生物过程蔓延开来。这些工具应用广泛，并可能带来对复杂问题的新见解因果机制。特别是，这项研究将评估隔离干预对流感样疾病传播和提出新的有效干预措施以抗击大流行蔓延。