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.

描述（由申请人提供）：识别疾病传播的基本网络属性 Peter J. Mucha，北卡罗来纳大学教堂山分校先进材料、纳米科学与技术研究所和卡罗莱纳跨学科应用数学中心数学系副教授项目摘要（摘要）网络分析植根于数学图论、统计学、社会科学、统计物理学、计算机科学和应用数学等跨学科领域，具有对疾病传播提出批判性见解的潜力。人群，跨越多种生物传播和行为影响机制。然而，为了切实影响未来的预测和行为干预，此类分析的结果不能依赖于有关整个潜在传染网络的完整和完美的信息。相反，将继续采用疾病在人群中传播的降阶模型；但这些模型将通过额外使用更有限的网络信息以及更好地了解哪些基本网络特征影响模型的预测和准确性而得到改进。这项拟议的研究计划利用并结合了网络分析两个领域的最新进展——网络耦合动力学的近似模型和新的社区检测技术——其具体目标是生成、探索和编目网络级别之间的一系列比较。疾病传播的模拟和降阶模型。支持活动将包括（1）开发社区感知子分区模型，以概括现有的网络感知系统，（2）新的多切片网络社区检测方法的算法改进，以及（3）专门针对社区检测的其他理论发展支持改进疾病传播模型的具体目标。与公共卫生的相关性在于有针对性地应用改进生物疾病和社会传染病传播的数学模型，强调识别准确建模所需的基本网络结构。通过识别与模型方程系统配对的基础网络的基本属性，本研究的结果将提供关于必须准确采样哪些网络属性以了解该人群的疾病动态的基本见解，以及对人群水平建模和未来的影响。干预多种疾病。