Calibrating deterministic compartmental models of infection dynamics using neural network and data sampling approaches
2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine (CSGB)(2022)
摘要
Pandemics caused by the new coronavirus has spread globally with a strong contagion rate and death rate. In this paper the deterministic SEIR model is calibrated with Metropolis Hasting algorithm, physics-informed neural network (PINN) and latin hypercube sampling (LHS) method to identify the optimal hyper parameters of SEIR model and to forecast the dynamics of COVID-19 incidence in Saint-Petersburg, Russia, its retrospective analysis and evaluation of the effectiveness of control measures.
更多查看译文
关键词
COVID-19,Metropolis Hastings,neural network,compartmental models
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要