LSTM-based Forecasting using Policy Stringency and Time-varying Parameters of the SIR Model for COVID-19
Author:
Affiliation:
1. University of Tsukuba,Degree Programs in Systems and Information Engineering Graduate School of Science and Technology,Tsukuba,Japan
2. University of Tsukuba,Faculty of Engineering, Information and Systems,Tsukuba,Japan
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10087343/10087366/10087773.pdf?arnumber=10087773
Reference27 articles.
1. A COVID-19 Pandemic Artificial Intelligence–Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study
2. SIR Model with Stochastic Transmission
3. A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning
4. An innovative ensemble model based on multiple neural networks and a novel heuristic optimization algorithm for COVID-19 forecasting
5. An Introduction to Compartmental Modeling for the Budding Infectious Disease Modeler
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. From Policy to Prediction: Assessing Forecasting Accuracy in an Integrated Framework with Machine Learning and Disease Models;Journal of Computational Biology;2024-08-02
2. Epidemic Modeling using Hybrid of Time-varying SIRD, Particle Swarm Optimization, and Deep Learning;2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT);2023-07-06
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