基于复杂网络的传染病溯源方法研究

Source tracing of infectious diseases (including rumors, trends) can help people to restore the spreading process and control the source rapidly, how to achieve the source positioning through the observation of node state in the network has become the research hotspot and difficulty in recent years. This project plans to use complex network to achieve the modeling of source-tracing problem, and in-depth research the source-tracing process quantization and system dynamics evolution mechanism under multiple state. First of all, on the basis of the analysis of spatial-temporal characteristics and relation network, then to determine the network key parameters and abstract standards and establish a source-tracing model of multidimensional complex network; Then, by analyzing the node status matrix of network timing state diagram, the source-tracing process is abstracted into a quantifiable trend with introduction of the node absorption probability; On the basis of the above work, then to execute the dynamic simulation to compare and verify the correctness and validity of these methods, and explore the key factors that influence the source-tracing range fast contraction, in order to contribute to the path restoration and blocking control of infectious diseases.

传染病（包括谣言，流行趋势）溯源能够为人们还原传播过程及快速控制源头提供有益帮助，如何通过对网络节点状态的观察来实现对源头的定位已成为近年来溯源研究的热点和难点。本项目拟利用复杂网络对传染病溯源问题进行建模，深入研究多时态下的溯源概率量化和相关动力学演化机理。首先，在传染病传播时空特征和关系网络分析的基础上，确定网络关键参数和抽象标准，建立一个多维的复杂网络溯源模型；然后，通过分析网络时序状态图的节点状态矩阵，并引入节点吸收概率将溯源过程抽象为一种可量化的收缩趋势；在此基础上，进行动力学演化仿真，对比验证上述方法的正确性与有效性，并挖掘影响溯源范围快速收缩的关键参数，以期对传染病传播路径还原和阻断控制做出贡献。

本项目利用复杂网络对传染病溯源问题进行建模，深入研究多时态下的溯源概率量化和相关动力学演化机理。首先，在传染病传播时空特征和关系网络分析的基础上，确定网络关键参数和抽象标准，建立一个多维的复杂网络溯源模型；然后，通过分析网络时序状态图的节点状态矩阵，并引入节点吸收概率将溯源过程抽象为一种可量化的收缩趋势；在此基础上，进行动力学演化仿真，并通过实验证明了该方法的正确性与有效性。由于微博博文的传播、计算机病毒的传播和传染病病毒的传播途径相似，故项目中也对微博博文以及计算机病毒的溯源进行了研究。通过对传染病、微博以及计算机病毒的溯源研究与分析，成功还原了它们的传播过程，并实现了源头查找，这为以后人们还原传染病、谣言等的传播过程及快速控制其源头提供了依据。