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The suppression effect of emotional contagion in the COVID-19 pandemic: A multi-layer hybrid modelling and simulation approach

机译:Covid-19大流行中情绪传染的抑制作用:多层混合模拟和仿真方法

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摘要

The entire world has suffered a lot since the outbreak of the novel coronavirus (COVID-19) in 2019, so simulation models of COVID-19 dynamics are urgently needed to understand and control the pandemic better. Meanwhile, emotional contagion, the spread of vigilance or panic, serves as a negative feedback to the epidemic, but few existing models take it into consideration. In this study, we proposed an innovative multi-layer hybrid modelling and simulation approach to simulate disease transmission and emotional contagion together. In each layer, we used a hybrid simulation method combining agent-based modelling (ABM) with system dynamics modelling (SDM), keeping spatial heterogeneity while reducing computation costs. We designed a new emotion dynamics model IWAN (indifferent, worried, afraid and numb) to simulate emotional contagion inside a community during an epidemic. Our model was well fit to the data of China, the UK and the US during the COVID-19 pandemic. If there weren’t emotional contagion, our experiments showed that the confirmed cases would increase rapidly, for instance, the total confirmed cases during simulation in Guangzhou, China would grow from 334 to 2096, which increased by 528%. We compared the calibrated emotional contagion parameters of different countries and found that the suppression effect of emotional contagion in China is relatively more visible than that in the US and the UK. Due to the experiment results, the proposed multi-layer network model with hybrid simulation is valid and can be applied to the quantitative analysis of the epidemic trends and the suppression effect of emotional contagion in different countries. Our model can be modified for further research to study other social factors and intervention policies in the COVID-19 pandemic or future epidemics.
机译:自2019年新型冠状病毒(Covid-19)爆发以来,整个世界遭受了很多遭受了很多潜力,因此迫切需要进行Covid-19动态的模拟模型来了解和控制大流行。同时,情绪传染,警惕或恐慌的传播,作为流行病的负面反馈,但很少有现有模型考虑。在这项研究中,我们提出了一种创新的多层混合模拟和模拟方法来模拟疾病传播和情绪传染。在每层中,我们使用了将基于代理的建模(ABM)与系统动态建模(SDM)结合的混合仿真方法,保持空间异质性,同时降低计算成本。我们设计了一种新的情感动态模型Iwan(无动于衷,担心,害怕,麻木),在流行病中模拟社区内的情绪传染。在Covid-19大流行期间,我们的模型适合中国,英国和美国的数据。如果没有情绪传染,我们的实验表明,确认的病例将迅速增加,例如,中国广州模拟中的总证实案件总量从334到2096年增长,增加了528%。我们比较了不同国家的校准情绪传染参数,发现中国在中国的情绪传染的抑制效果比美国和英国的情绪传染相对更明显。由于实验结果,具有混合模拟的所提出的多层网络模型是有效的,可以应用于不同国家情绪传染的流行趋势的定量分析。我们的模型可以被修改以进一步研究,以研究Covid-19大流行或未来流行病中的其他社会因素和干预政策。

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