Multi-scale approach to disease spreading in social networks

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长期以来已经确定了少数潜在的社会机制，个体在相互作用的过程中遵循这些机制。在这个项目中，我们建议进行多尺度分析，以调查这些微观层面的个人行动的社会机制是如何转化为网络模式在大尺度上，反过来又决定了传染病在社会网络中的传播。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关系的过程。这将揭示作为传染病传播支持的社会网络的全球结构。这些信息将在以后被用来降低免疫接种的门槛，通过识别那些个人，通过他们在网络中的位置，比平均水平更有可能将疾病传播到社会网络的更大部分。 我们的项目的新奇在于多尺度方法，我们不断地将信息从局部社会机制转移到全球网络属性，根据以下层次：个人行动（微观层面）！全球连接网络（宏观层面）！疾病传播动态，以确定疾病传播最快的传播者（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，在那里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。 在真实的社区的连接网络将分析在国家的最先进的网络理论。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的疾病传播。该提案的创新之处在于将新的系统科学/统计物理工具引入与社会学相关的健康问题。该项目是具有复杂科学专业知识的物理学家（Makse）和社会学家（Liljeros）之间的合作，他们将提供多学科领域的广泛专业知识，以便从科学的不同立场评估结果。

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.