Department of Mathematics and Physics, College of Science, Zhengzhou University of Aeronautics, Zhengzhou 450015, PR China.
School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, Kuala Terengganu 21030, Malaysia.
Math Biosci. 2018 Feb;296:98-112. doi: 10.1016/j.mbs.2017.12.002. Epub 2017 Dec 19.
Mass-media reports on an epidemic or pandemic have the potential to modify human behaviour and affect social attitudes. Here we construct a Filippov model to evaluate the effects of media coverage and quarantine on the transmission dynamics of influenza. We first choose a piecewise smooth incidence rate to represent media reports being triggered once the number of infected individuals exceeds a certain critical level [Formula: see text] . Further, if the number of infected cases increases and exceeds another larger threshold value [Formula: see text] ( [Formula: see text] ), we consider that the incidence rate tends to a saturation level due to the protection measures taken by individuals; meanwhile, we begin to quarantine susceptible individuals when the number of susceptible individuals is larger than a threshold value S. Then, for each susceptible threshold value S, the global properties of the Filippov model with regard to the existence and stability of all possible equilibria and sliding-mode dynamics are examined, as we vary the infected threshold values [Formula: see text] and [Formula: see text] . We show generically that the Filippov system stabilizes at either the endemic equilibrium of the subsystem or the pseudoequilibrium on the switching surface or the endemic equilibrium [Formula: see text] depending on the choice of the threshold values. The findings suggest that proper combinations of infected and susceptible threshold values can maintain the number of infected individuals either below a certain threshold level or at a previously given level.
大众媒体对传染病或大流行病的报道有可能改变人类行为并影响社会态度。在这里,我们构建了一个 Filippov 模型来评估媒体报道和隔离对流感传播动力学的影响。我们首先选择一个分段光滑的感染率来表示一旦感染人数超过某个临界水平 [Formula: see text] ,媒体报道就会被触发。此外,如果感染病例数量增加并超过另一个更大的阈值 [Formula: see text] ( [Formula: see text] ),由于个体采取的保护措施,我们认为感染率趋于饱和水平;同时,当易感个体数量超过阈值 S 时,我们开始对易感个体进行隔离。然后,对于每个易感阈值 S,我们会根据感染阈值 [Formula: see text] 和 [Formula: see text] 的变化,检查 Filippov 模型关于所有可能平衡点的存在性和稳定性以及滑动模态动力学的全局性质。我们一般表明,Filippov 系统要么在子系统的地方病平衡点处稳定,要么在切换表面上的伪平衡点处稳定,要么在地方病平衡点 [Formula: see text] 处稳定,具体取决于阈值的选择。研究结果表明,适当组合感染和易感阈值可以将感染人数保持在一定的阈值以下或保持在以前给定的水平。