scholarly journals Global stability of an SIS epidemic model with feedback mechanism on networks

2018 ◽  
Vol 2018 (1) ◽  
Author(s):  
Xiaodan Wei ◽  
Gaochao Xu ◽  
Wenshu Zhou
2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Lijuan Chen ◽  
Shouying Huang ◽  
Fengde Chen ◽  
Mingjian Fu

AbstractIt is well known that the feedback mechanism or the individual’s intuitive response to the epidemic can have a vital effect on the disease’s spreading. In this paper, we investigate the bifurcation behavior and the optimal feedback mechanism for an SIS epidemic model on heterogeneous networks. Firstly, we present the bifurcation analysis when the basic reproduction number is equal to unity. The direction of bifurcation is also determined. Secondly, different from the constant coefficient in the existing literature, we incorporate a time-varying feedback mechanism coefficient. This is more reasonable since the initiative response of people is constantly changing during different process of disease prevalence. We analyze the optimal feedback mechanism for the SIS epidemic network model by applying the optimal control theory. The existence and uniqueness of the optimal control strategy are obtained. Finally, a numerical example is presented to verify the efficiency of the obtained results. How the topology of the network affects the optimal feedback mechanism is also discussed.


OALib ◽  
2017 ◽  
Vol 04 (05) ◽  
pp. 1-9
Author(s):  
Xiongding Liu ◽  
Tao Li ◽  
Yuanmei Wang ◽  
Chen Wan ◽  
Jing Dong

2018 ◽  
Vol 41 (13) ◽  
pp. 5345-5354
Author(s):  
Kei Fushimi ◽  
Yoichi Enatsu ◽  
Emiko Ishiwata

2011 ◽  
Vol 204-210 ◽  
pp. 354-358 ◽  
Author(s):  
Guang Wu Gong ◽  
Da Min Zhang

A new susceptible-infected-susceptible model with feedback mechanism is proposed. The dynamic behavior of the epidemic model with feedback mechanism in scale-free networks is researched by theoretical analysis and computer simulation. The results show that feedback mechanism can reduce the stable infective ratio of system; however, it can not influence the epidemic threshold of system. The results can help us to understand rightly epidemic spreading process in reality networks and guide people to design effective epidemic preventive and controlling measures when epidemic outbreaks.


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