Center for Infectious Disease Dynamics, Penn State University, University Park, PA, USA.
Sci Rep. 2013;3:1905. doi: 10.1038/srep01905.
Social network analysis is now widely used to investigate the dynamics of infectious disease spread. Vaccination dramatically disrupts disease transmission on a contact network, and indeed, high vaccination rates can potentially halt disease transmission altogether. Here, we build on mounting evidence that health behaviors - such as vaccination, and refusal thereof - can spread across social networks through a process of complex contagion that requires social reinforcement. Using network simulations that model health behavior and infectious disease spread, we find that under otherwise identical conditions, the process by which the health behavior spreads has a very strong effect on disease outbreak dynamics. This dynamic variability results from differences in the topology within susceptible communities that arise during the health behavior spreading process, which in turn depends on the topology of the overall social network. Our findings point to the importance of health behavior spread in predicting and controlling disease outbreaks.
社交网络分析现在被广泛用于研究传染病传播的动态。疫苗接种极大地破坏了接触网络上的疾病传播，事实上，高疫苗接种率有可能完全阻止疾病传播。在这里，我们根据越来越多的证据，即健康行为（如疫苗接种和拒绝接种）可以通过一种需要社会强化的复杂传染过程在社交网络上传播。使用模拟健康行为和传染病传播的网络模拟，我们发现，在其他条件相同的情况下，健康行为传播的过程对疾病爆发动态有很强的影响。这种动态可变性源于健康行为传播过程中易感群体内部拓扑结构的差异，而这种差异又取决于整个社交网络的拓扑结构。我们的研究结果表明，健康行为传播在预测和控制疾病爆发方面非常重要。
