本课题主要研究SIR（Susceptible-Infective-Removal）扩散网络的自组织斑图的拓扑结构、形成机制、生物机制以及控制方法。首先结合一般反应扩散方程理论、稳定性理论、网络理论以及矩阵理论，研究扩散网络下SIR模型的自组织斑图的动力学机制与拓扑结构的关系以及控制的方法。然后结合随机和时滞动力学理论，研究SIR扩散网络受到随机及时滞等外部因素影响时，传染病网络的内部因素和外部因素耦合使得传染病的分布重新组织的行为及如何利用外部刺激（随机及时滞等）达到控制SIR系统内部不同机制的产生和消失。最后把理论结果应用于实际问题中，利用公开的传染病数据，结合大数据建模的方法，把上述理论和传染病模型传播现象结合起来，揭示系统内部自组织行为的生物机制及其控制方法，尤其是系统涌现出的不同斑图机制同传染病扩散机制的关系，通过控制理论提出精准控制传染病的策略，为科学防治疫情提供理论参考及指导。

This project studies the topology structure, formation mechanism, biological mechanism, and control method of self-organizing pattern formation of the SIR model induced by the diffusion networks. We first investigate the relationship between dynamic mechanism and the topological structure using the tools of general reaction-diffusion equation theory, stability theory, complex network theory, and matrix theory. Additionally, we study the methods of control for pattern formation in this model in order to prevent epidemics. To achieve our goal, we consider the re-organizing behavior induced by the internal (i.e. characteristic of the virus) and external factors (i.e. social factors or noise or delay) of the infectious disease network according to stochastic and delay dynamics theory. We try to propose an approach to control the generation and disappearance of different mechanisms through stimulations. At the end, the validation of theoretical results will be done with big data modeling. This analysis reveals the biological mechanism and control method of self-organizing behavior within the system, particularly the relationship between the different pattern mechanisms. In this project, precise control of infectious diseases is analyzed using the relevant mathematical tools, which provides theoretical reference and guidance for the scientific prevention and control of the epidemic.