Identifying essential network properties for disease spread

识别疾病传播的基本网络属性

基本信息

批准号：
8289402
负责人：
Peter John Mucha
金额：
$ 18.22万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-08-01 至 2014-07-31
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): Identifying Essential Network Properties for Disease Spread Peter J. Mucha, Associate Professor, Department of Mathematics, Institute for Advanced Materials, Nanoscience and Technology, & Carolina Center for Interdisciplinary Applied Mathematics, University of North Carolina at Chapel Hill Project Summary (Abstract) Interdisciplinarily rooted across mathematical graph theory, statistics, the social sciences, statistical physics, computer science, and applied mathematics, network analysis holds the potential to make critical insights about the spread of disease in a population, across a variety of mechanisms of biological transmission and behavioral influence. However, to realistically influence future prediction and behavioral intervention, the results of such analysis must not rely on complete and perfect information about the entire underlying network of contagion. Instead, reduced-order mod els of disease spread within the population will continue to be employed; but those models will be improved by additional use of more limited network information, and by an improved understanding about which essential network features influence the predictions and accuracy of models. This proposed research program leverages and combines recent advances in two areas of net- work analysis-approximate models of network-coupled dynamics and new community detection technologies-with the specific aim of generating, exploring, and cataloguing a family of comparisons between network-level simulations and reduced-order models of disease spread. Supporting activities will include (1) development of community-aware sub compartmented models which generalize existing network-aware systems, (2) algorithmic improvement of the new multislice network community detection method, and (3) additional theoretical developments in community detection specifically targeted to support the specific aim of improved modeling of disease spread. The relevance to public health is in the targeted application to improved mathematical modeling of the spread of both biological diseases and social contagions, emphasizing the identification of the essential network structures necessary for accurate modeling. By identifying the essential properties of the underlying networks paired with model equation systems, the results of this study will provide fundamental insight about which network properties must be accurately sampled to understand the disease dynamics in that population, with future implications for population-level modeling and intervention across a wide variety of diseases.

DESCRIPTION (provided by applicant): Identifying Essential Network Properties for Disease Spread Peter J. Mucha, Associate Professor, Department of Mathematics, Institute for Advanced Materials, Nanoscience and Technology, & Carolina Center for Interdisciplinary Applied Mathematics, University of North Carolina at Chapel Hill Project Summary (Abstract) Interdisciplinarily rooted across mathematical graph theory, statistics, the social sciences, statistical physics, computer science, and applied数学，网络分析具有对疾病在人群中的传播以及各种生物传播和行为影响机制的重要见解。但是，要现实地影响未来的预测和行为干预，这种分析的结果必须不依赖于整个基础传播网络的完整而完美的信息。取而代之的是，将继续使用疾病蔓延的疾病传播的减少。但是，通过额外使用更多有限的网络信息，以及对哪些基本网络特征影响模型的预测和准确性，可以通过额外使用更多有限的网络信息来改善这些模型。该拟议的研究计划利用并结合了网络耦合动力学和新的社区检测技术模型的两个领域的最新进展，以及生成，探索和分类网络级别模拟与疾病模型降低疾病模型之间的比较家族的具体目的。支持活动将包括（1）开发社区感知的子隔室模型，这些模型推广了现有的网络感知系统，（2）新的多层性网络社区检测方法的算法改进，以及（3）专门针对旨在支持改进疾病蔓延的特定目标的社区检测中的其他理论发展。与公共卫生的相关性在于针对性应用于改进生物疾病和社会传染的数学建模，强调确定准确建模所需的基本网络结构。通过确定与模型方程式配对的基本网络的基本特性，这项研究的结果将提供基本的见解，以了解必须准确采样哪些网络特性，以了解该人群中的疾病动态，并将来对各种疾病的人口水平的建模和干预效果。