Multi-scale approach to disease spreading in social networks

社交网络中疾病传播的多尺度方法

• 批准号: 8570153
• 负责人: HERNAN MAKSE
• 金额: $ 20.66万
• 依托单位: CITY COLLEGE OF NEW YORK
• 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 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8570153
• 关键词: Address; Affect; Behavior; Behavioral Research; Collaborations; Communicable Diseases; Communities; Complex; Data; Databases; Disease; Disease Outbreaks; Epidemic; Evolution; Goals; Grant; Graph; Health; Immunization; Individual; Lead; Link; Location; Measures; Methods; Microscopic; Monitor; Names; Pathway Analysis; Pattern; Physics; Play; Population; Positioning Attribute; Process; Property; Public Health; Resources; Scheme; Science; Scientist; Shapes; Social Interaction; Social Network; Social Work; Societies; Structure; System; Testing; Time; Vaccinated; base; cooperative study; design; direct application; disease transmission; improved; innovation; member; novel; public health relevance; social; social model; sociologist; theories; tool

DESCRIPTION (provided by applicant): Multi-scale approach to disease spreading in social net- works Social scientists have long identified a small number of underlying social mechanisms which individuals follow in the course of interactions with each other. In this project we propose to perform a multi-scale analysis to investigate how these micro-level social mechanisms of individual actions are transformed into network patterns at the large-scale that, in turn, determines the spread of infectious diseases in a social network. We will develop a systematic study of individual interactions in social communities, with the goal of understanding the process of establishing new network ties. This will reveal the global structure of social network as the support for the spreading of infectious diseases. This information will be later used to lower the threshold of immunization by identifying those individuals who, through their position in the network, are more likely than average to spread the disease to the larger portion of the social network. The novelty of our project lies in the multi-scale approach, where we continuously transfer information from local social mechanisms to global network properties, according to the following layers: individual actions (micro- scopic level)! global network of connections (macro-level)! disease spreading dynamics to identify the fastest spreaders of disease (superspreaders). Our approach has the novel value of studying the real-time dynamics of connections where we can directly monitor exact timing of every new tie in the network, i.e., how this dynamics affects the spread of disease. The network of connections in real communities will be analyzed in terms of state-of-the-art network theory. A large number of valuable tools from statistical network analysis will inform us about the multi-scale structural properties of the social networks and how these properties affect the transmission of disease in a network. The innovation of the proposal lies in the introduction of novel Systems Science/Statistical Physics tools to sociologically- relevant health problems. The project is a collaboration between a physicist with expertise in Complex Science (Makse) and a sociologist (Liljeros) who will provide the wide range of expertise in multi-disciplinary domains necessary to evaluate the results from scientifically varying standpoints.

描述（由申请人提供）：社会网络中疾病传播的多尺度方法 社会科学家早已确定了个体在彼此互动过程中遵循的少数潜在社会机制。在这个项目中，我们建议进行多尺度分析，以研究这些个体行为的微观社会机制如何转化为大规模的网络模式，进而决定传染病在社交网络中的传播。我们将对社会社区中的个人互动进行系统研究，目的是了解建立新网络联系的过程。这将揭示社交网络的全球结构作为传染病传播的支持。该信息稍后将用于通过识别那些通过其在网络中的位置比平均水平更有可能将疾病传播到社交网络的较大部分的个人来降低免疫门槛。 我们项目的新颖性在于多尺度方法，我们根据以下层次不断地将信息从本地社会机制转移到全球网络属性：个人行动（微观层面）！全球连接网络（宏观层面）！疾病传播动态，以确定最快的疾病传播者（超级传播者）。我们的方法具有研究连接的实时动态的新颖价值，我们可以直接监控网络中每个新连接的确切时间，即这种动态如何影响疾病的传播。 将根据最先进的网络理论来分析真实社区中的连接网络。统计网络分析中的大量有价值的工具将告诉我们社交网络的多尺度结构特性以及这些特性如何影响网络中疾病的传播。该提案的创新之处在于将新颖的系统科学/统计物理工具引入到社会学相关的健康问题中。该项目是一位具有复杂科学专业知识的物理学家 (Makse) 和一位社会学家 (Liljeros) 之间的合作，他们将提供多学科领域所需的广泛专业知识，以从不同的科学角度评估结果。