Centre for Complex Systems, Faculty of Engineering, University of Sydney, Sydney, NSW, 2006, Australia.
Marie Bashir Institute for Infectious Diseases and Biosecurity, University of Sydney, Westmead, NSW, 2145, Australia.
Sci Rep. 2020 May 6;10(1):7646. doi: 10.1038/s41598-020-64183-1.
Understanding the impact of behavior dependent mobility in the spread of epidemics and social disorders is an outstanding problem in computational epidemiology. We present a modelling approach for the study of mobility that adapts dynamically according to individual state, epidemic/social-contagion state and network topology in accordance with limited data and/or common behavioral models. We demonstrate that even for simple compartmental network processes, our approach leads to complex spatial patterns of infection in the endemic state dependent on individual behavior. Specifically, we characterize the resulting phenomena in terms of phase separation, highlighting phase transitions between distinct spatial states and determining the systems' phase diagram. The existence of such phases implies that small changes in the populations' perceptions could lead to drastic changes in the spatial extent and morphology of the epidemic/social phenomena.
理解行为相关的移动性对传染病和社会混乱传播的影响是计算流行病学中的一个突出问题。我们提出了一种针对移动性的建模方法，该方法可以根据个体状态、传染病/社会传播状态以及网络拓扑结构进行动态调整，以适应有限的数据和/或常见的行为模型。我们证明，即使对于简单的分区网络过程，我们的方法也会导致在传染病的地方病状态下，感染的空间模式会根据个体行为而变得复杂。具体来说，我们根据相分离来描述所产生的现象，突出不同空间状态之间的相变，并确定系统的相图。这些相的存在意味着，人群认知的微小变化可能导致传染病/社会现象的空间范围和形态发生巨大变化。
