Impact of network topology on the spread of infectious diseases

ABSTRACT The complex network theory constitutes a natural support for the study of a disease propagation. In this work, we present a study of an infectious disease spread with the use of this theory in combination with the Individual Based Model. More specifically, we use several complex network models widely known in the literature to verify their topological effects in the propagation of the disease. In general, complex networks with different properties result in curves of infected individuals with different behaviors, and thus, the growth of a given disease is highly sensitive to the network model used. The disease eradication is observed when the vaccination strategy of 10% of the population is used in combination with the random, small world or modular network models, which opens an important space for control actions that focus on changing the topology of a complex network as a form of reduction or even elimination of an infectious disease.摘要：复杂网络理论为疾病传播研究提供了天然的理论支撑。本研究将该理论与基于个体的模型（Individual Based Model）相结合，开展传染病传播规律研究。具体而言，本研究选用文献中广为熟知的多款复杂网络模型，验证其拓扑结构对疾病传播的影响效应。总体而言，具备不同属性的复杂网络会使得感染者人数变化曲线呈现各异的演化特征，因此特定传染病的传播态势对所采用的网络模型具有高度敏感性。当针对10%的人群实施疫苗接种策略，并配合随机网络、小世界（small world）网络或模块化网络模型时，可实现疾病根除，这为以改变复杂网络拓扑结构作为降低乃至消除传染病的防控手段开辟了重要的研究空间。