2012/07/04 by Panpan Shu, Ming Tang, Shu, Panpan +5 · 1 citation
Medicine · Physics and Astronomy · #65Zxx #82Dxx #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Mathematical and Theoretical Epidemiology and Ecology Models #Opinion Dynamics and Social Influence #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #Statistical Mechanics (cond-mat.stat-mech)
paper · pdf · doi:10.48550/arxiv.1207.0931
openalex publication_date 2012/07/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Weak ties play a significant role in the structures and the dynamics of community networks. Based on the susceptible-infected model in contact process, we study numerically how weak ties influence the predictability of epidemic dynamics. We first investigate the effects of different kinds of weak ties on the variabilities of both the arrival time and the prevalence of disease, and find that the bridgeness with small degree can enhance the predictability of epidemic spreading. Once weak ties are settled, compared with the variability of arrival time, the variability of prevalence displays a diametrically opposed changing trend with both the distance of the initial seed to the bridgeness and the degree of the initial seed. More specifically, the further distance and the larger degree of the initial seed can induce the better predictability of arrival time and the worse predictability of prevalence. Moreover, we discuss the effects of weak tie number on the epidemic variability. As community strength becomes very strong, which is caused by the decrease of weak tie number, the epidemic variability will change dramatically. Compared with the case of hub seed and random seed, the bridgenss seed can result in the worst predictability of arrival time and the best predictability of prevalence. These results show that the variability of arrival time always marks a complete reversal trend of that of prevalence, which implies it is impossible to predict epidemic spreading in the early stage of outbreaks accurately.