Social contact networks and the way people interact with each other are the key factors that impact on epidemics spreading. However, it is challenging to model the behavior of epidemics based on social contact networks due to their high dynamics. Traditional models such as susceptible-infected-recovered (SIR) model ignore the crowding or protection effect and thus has some unrealistic assumption. In this paper, we consider the crowding or protection effect and develop a novel model called improved SIR model. Then, we use both deterministic and stochastic models to characterize the dynamics of epidemics on social contact networks. The results from both simulations and real data set conclude that the epidemics are more likely to outbreak on social contact networks with higher average degree. We also present some potential immunization strategies, such as random set immunization, dominating set immunization, and high degree set immunization to further prove the conclusion.

中文翻译：

---

建模流行病在社交联系网络上的传播

社会联系网络和人们相互交流的方式是影响流行病传播的关键因素。然而，由于其高动态性，基于社交联系网络对流行病的行为进行建模具有挑战性。传统模型如易感感染恢复（SIR）模型忽略了拥挤或保护效应，因此存在一些不切实际的假设。在本文中，我们考虑了拥挤或保护效应，并开发了一种称为改进 SIR 模型的新模型。然后，我们使用确定性和随机模型来表征社交联系网络上流行病的动态。模拟和真实数据集的结果都得出结论，流行病更有可能在平均程度较高的社交网络上爆发。我们还介绍了一些潜在的免疫策略，