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A dynamic approach to support outbreak management using reinforcement learning and semi-connected SEIQR models
Yamin Kao, Po Jui Chu, Pai Chien Chou,
Chien Chang Chen
認知智慧與精準健康照護研究中心
生醫科學與工程學系
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Keyphrases
Dynamic Approach
100%
Reinforcement Learning
100%
Infected Cases
100%
Semi-connected
100%
Outbreak Management
100%
Reinforcement Learning Environments
60%
Okinawa
40%
Epidemic Wave
40%
Timing Analysis
20%
Number of Peaks
20%
Economic Activity
20%
Interactive Environments
20%
Reward Function
20%
Daily Monitoring
20%
Reinforcement Learning Algorithm
20%
Tokyo
20%
Policy Making
20%
Global Economic Crisis
20%
COVID-19 Outbreak
20%
COVID-19 Spread
20%
Disease Control
20%
Human Flow
20%
Tokugawa Japan
20%
Control Activities
20%
Osaka
20%
Connected Region
20%
Hokkaido Japan
20%
Receiving Feedback
20%
Containment Measures
20%
Trained Agent
20%
Restrictions on Movement
20%
Agent Performance
20%
Personalized Screening
20%
Transport Hub
20%
Disease Economics
20%
Reinforcement Learning Agent
20%
Action Timing
20%
Synthetic Population
20%
Individual Movement
20%
Population-weighted Density
20%
Mathematics
Connected Semi
100%
Dynamic Approach
100%
Connected Region
50%
Economics, Econometrics and Finance
Learning Environment
100%
Global Economic Crisis
33%
Pharmacology, Toxicology and Pharmaceutical Science
Disease
100%
Chemical Engineering
Reinforcement Learning
100%
Neuroscience
Reinforcement Learning
100%