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The Effects of Imitation Dynamics on Vaccination Behaviours in SIR-Network Model

机译:模拟动力学对SIR网络模型中疫苗接种行为的影响

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摘要

We present a series of SIR-network models, extended with a game-theoretic treatment of imitation dynamics which result from regular population mobility across residential and work areas and the ensuing interactions. Each considered SIR-network model captures a class of vaccination behaviours influenced by epidemic characteristics, interaction topology, and imitation dynamics. Our focus is the resultant vaccination coverage, produced under voluntary vaccination schemes, in response to these varying factors. Using the next generation matrix method, we analytically derive and compare expressions for the basic reproduction number R0 for the proposed SIR-network models. Furthermore, we simulate the epidemic dynamics over time for the considered models, and show that if individuals are sufficiently responsive towards the changes in the disease prevalence, then the more expansive travelling patterns encourage convergence to the endemic, mixed equilibria. On the contrary, if individuals are insensitive to changes in the disease prevalence, we find that they tend to remain unvaccinated. Our results concur with earlier studies in showing that residents from highly connected residential areas are more likely to get vaccinated. We also show that the existence of the individuals committed to receiving vaccination reduces R0 and delays the disease prevalence, and thus is essential to containing epidemics.
机译:我们提出了一系列的SIR网络模型,并扩展了对模仿动态的博弈论处理,这是由于人口在居住区和工作区的正常流动以及随之而来的相互作用所致。每个考虑的SIR网络模型都捕获一类受流行病特征,相互作用拓扑和仿制动力学影响的疫苗接种行为。我们的重点是针对这些变化的因素,根据自愿疫苗接种计划产生的疫苗接种覆盖率。使用下一代矩阵方法,我们可以分析得出并比较基本再现数 R 0 用于建议的SIR网络模型。此外,我们针对所考虑的模型模拟了随时间变化的流行趋势,并表明,如果个人对疾病流行率的变化有足够的反应,那么更广泛的出行方式将促使人们趋向于流行,混合的均衡。相反,如果个体对疾病患病率的变化不敏感,我们发现他们倾向于不接种疫苗。我们的结果与较早的研究一致,该研究表明,居住在高度连通的居民区的居民更容易接种疫苗。我们还表明,致力于接受疫苗接种的个体的存在减少了 <增长> R 0 并延迟疾病的流行,因此对于控制流行病至关重要。

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