Turker Meliksah, Bingol Haluk O
Department of Computer Engineering, Bogazici University, Istanbul, 34342 Turkey.
Eur Phys J B. 2023;96(2):16. doi: 10.1140/epjb/s10051-023-00484-4. Epub 2023 Feb 6.
The last three years have been an extraordinary time with the COVID-19 pandemic killing millions, affecting and distressing billions of people worldwide. Authorities took various measures such as turning school and work to remote and prohibiting social relations via curfews. In order to mitigate the negative impact of the epidemics, researchers tried to estimate the future of the pandemic for different scenarios, using forecasting techniques and epidemics simulations on networks. Intending to better represent the real-life in an urban town in high resolution, we propose a novel multi-layer network model, where each layer corresponds to a different interaction that occurs daily, such as "household", "work" or "school". Our simulations indicate that locking down "friendship" layer has the highest impact on slowing down epidemics. Hence, our contributions are twofold, first we propose a parametric network generator model; second, we run SIR simulations on it and show the impact of layers.
过去三年是一段非同寻常的时期，新冠疫情导致数百万人死亡，影响并困扰了全球数十亿人。当局采取了各种措施，如将学校和工作转为远程模式，并通过宵禁禁止社交活动。为了减轻疫情的负面影响，研究人员试图利用预测技术和网络上的疫情模拟，针对不同情况估计疫情的未来发展。为了以高分辨率更好地呈现城镇的现实生活，我们提出了一种新颖的多层网络模型，其中每一层对应于日常发生的不同互动，如“家庭”“工作”或“学校”。我们的模拟表明，封锁“友谊”层对减缓疫情的影响最大。因此，我们的贡献有两方面，首先我们提出了一个参数化网络生成器模型；其次，我们在该模型上运行SIR模拟并展示各层的影响。
