Multi-scale approach to disease spreading in social networks

描述(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就确定了个人在相互作用过程中遵循的少量潜在的社会机制。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为的社会机制如何在大范围内转化为网络模式，进而决定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，目的是了解建立新型网络联系的过程。这将揭示作为传染病传播支撑的社会网络的全球结构。这些信息稍后将用于通过识别那些通过其在网络中的位置更有可能将疾病传播到社会网络的较大部分的个人来降低免疫门槛。我们项目的新奇之处在于多尺度的方法，我们根据以下几个层次不断地将信息从当地的社会机制传递到全球的网络属性：个人行动(微观层面)！全球连接网络(宏观层面)！疾病传播动力学，以确定疾病传播最快的人(超级传播者)。我们的方法具有研究连接的实时动态的新价值，我们可以直接监控网络中每一个新连接的准确计时，即这种动态如何影响疾病的传播。我们将根据最先进的网络理论来分析真实社区中的连接网络。来自统计网络分析的大量有价值的工具将告诉我们社会网络的多尺度结构属性，以及这些属性如何影响疾病在网络中的传播。该提案的创新之处在于引入了新的系统科学/统计物理学工具来解决与社会学有关的健康问题。该项目是一名在复杂科学方面有专长的物理学家(Makse)和一名社会学家(LilJeros)之间的合作，后者将提供从不同科学观点评估结果所需的多学科领域的广泛专业知识。

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.