The coevolution of contagion and behavior with increasing and decreasing awareness

被引:8
|
作者
Maghool, Samira [1 ,3 ]
Maleki-Jirsaraei, Nahid [1 ]
Cremonini, Marco [2 ]
机构
[1] Alzahra Univ, Phys Dept, Complex Syst Lab, Tehran, Iran
[2] Univ Milan, Dept Social & Polit Sci, Milan, Italy
[3] Univ Milan, Comp Sci Dept, Milan, Italy
来源
PLOS ONE | 2019年 / 14卷 / 12期
基金
欧盟地平线“2020”;
关键词
SITUATION AWARENESS; COMPLEX NETWORKS; DYNAMICS; MODEL; SPREAD; DISEASE; IMPACT;
D O I
10.1371/journal.pone.0225447
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Understanding the effects of individual awareness on epidemic phenomena is important to comprehend the coevolving system dynamic, to improve forecasting, and to better evaluate the outcome of possible interventions. In previous models of epidemics on social networks, individual awareness has often been approximated as a generic personal trait that depends on social reinforcement, and used to introduce variability in state transition probabilities. A novelty of this work is to assume that individual awareness is a function of several contributing factors pooled together, different by nature and dynamics, and to study it for different epidemic categories. This way, our model still has awareness as the core attribute that may change state transition probabilities. Another contribution is to study positive and negative variations of awareness, in a contagion-behavior model. Imitation is the key mechanism that we model for manipulating awareness, under different network settings and assumptions, in particular regarding the degree of intentionality that individuals may exhibit in spreading an epidemic. Three epidemic categories are considered-disease, addiction, and rumor-to discuss different imitation mechanisms and degree of intentionality. We assume a population with a heterogeneous distribution of awareness and different response mechanisms to information gathered from the network. With simulations, we show the interplay between population and awareness factors producing a distribution of state transition probabilities and analyze how different network and epidemic configurations modify transmission patterns.
引用
收藏
页数:22
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