Edge-based compartmental modelling of an SIR epidemic on a dual-layer static-dynamic multiplex network with tunable clustering
Rosanna C Barnard, Istvan Z Kiss, Luc Berthouze, Joel C Miller
备注：
29 pages, 12 Figures. Submitted to Bulletin of Mathematical Biology on 29/03/18. Version 2 has minor changes to the content: a reduced and edited abstract, the author summary is removed, acknowledgements were edited, references to the matrix G were bolded, some Figure titles and captions have been edited, and a reference to providing Matlab code at the end of the Methods section has been removed
The duration, type and structure of connections between individuals in real-world populations play a crucial role in how diseases invade and spread. Here, we incorporate the aforementioned heterogeneities into a model by considering a dual-layer static-dynamic multiplex network. The static network layer affords tunable clustering and describes an individual's permanent community structure. The dynamic network layer describes the transient connections an individual makes with members of the wider population by imposing constant edge rewiring. We follow the edge-based compartmental modelling approach to derive equations describing the evolution of a susceptible - infected - recovered (SIR) epidemic spreading through this multiplex network of individuals. We derive the basic reproduction number, measuring the expected number of new infectious cases caused by a single infectious individual in an otherwise susceptible population. We validate model equations by showing convergence to pre-existing edge-based compartmental model equations in limiting cases and by comparison with stochastically simulated epidemics. We explore the effects of altering model parameters and multiplex network attributes on resultant epidemic dynamics. We validate the basic reproduction number by plotting its value against associated final epidemic sizes measured from simulation and predicted by model equations for a number of setups. Further, we explore the effect of varying individual model parameters on the basic reproduction number. We conclude with a discussion of the significance and interpretation of the model and its relation to existing research literature. We highlight intrinsic limitations and potential extensions of the present model and outline future research considerations, both experimental and theoretical.
Analysis of non pharmaceutical interventions with SIR epidemic models: decreasing the infection peak vs. minimizing the epidemic size
Instituto Balseiro, Universidad Nacional de Cuyo（巴塞尔罗研究所，门多萨国立大学）；Comisión Nacional de Energía Atómica（国家原子能委员会）；Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)（国家科学研究委员会）；Centro Atómico Bariloche (CNEA)（巴里洛切原子中心）
Modeling Epidemics on Multiplex Networks: Epidemic Threshold and Basic Reproduction Number
Instituto Balseiro, Universidad Nacional de Cuyo（昆都萨国立大学巴尔塞罗研究所）；Centro Atómico Bariloche, Comisión Nacional de Energía Atómica（阿根廷国家原子能委员会巴里洛切原子中心）；Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)（阿根廷国家科学研究委员会）；Universidad Nacional del Centro de la Provincia de Buenos Aires, Facultad de Cs. Exactas, Instituto Multidisciplinario Sobre Ecosistemas Y Desarrollo Sustentable（布宜诺斯艾利斯省中部国立大学理学院跨学科生态系统与可持续发展研究所）；Universidad Nacional del Centro de la Provincia de Buenos Aires, Facultad de Cs. Exactas, Instituto PLADEMA（布宜诺斯艾利斯省中部国立大学理学院PLADEMA研究所）
Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks
Frankfurt Institute for Advanced Studies（法兰克福高等研究院）；Xidian-FIAS International Joint Research Center（西安电子科技大学-法兰克福高等研究院国际联合研究中心）；Institut für Theoretische Physik, Goethe Universität Frankfurt（法兰克福大学理论物理研究所）；GSI Helmholtzzentrum für Schwerionenforschung GmbH（GSI亥姆霍兹重离子研究中心）；Xidian University（西安电子科技大学）
本文提出将嵌入 SIR 模型的物理信息神经网络（PINN）用于传染病动力学建模，通过在合成数据和德国 COVID-19 数据上的实验证明其能准确识别和预测疫情传播趋势，展示了机器学习与仓室模型结合研究流行病的高潜力。
Unifying Physics- and Data-Driven Modeling via Novel Causal Spatiotemporal Graph Neural Network for Interpretable Epidemic Forecasting
Frankfurt Institute for Advanced Studies（法兰克福高等研究院）；Goethe Universität Frankfurt（法兰克福大学）；Xidian-FIAS International Joint Research Center（西电-法兰克福高等研究院国际联合研究中心）；School of Science and Engineering, The Chinese University of Hong Kong（香港中文大学（深圳）理工学院）；GSI Helmholtzzentrum für Schwerionenforschung GmbH（GSI亥姆霍兹重离子研究中心）
