Predicting the COVID19 Trajectory with a Simulation Deep Reinforcement Learning Approach
Document Type
Conference Proceeding
Publication Date
1-1-2022
Abstract
COVID19 pandemic has severely impacted every government and citizen of the world. At the same time, it has emphasized the significance of governmental decisionmaking when facing a sudden outbreak. In this paper, we aim to aid governments in addressing the difficult problem of epidemic control planning by providing a disease trajectory and economic impacts. We study a SimulationDeep Reinforcement Learning (SiRL) methodology to predict the COVID19 pandemic's trajectory for the next three months considering different intervention strategies. Our experiments show that if no action is taken, and the current rate of vaccination is assumed, the daily cases could see an increase of 145%. Our Reinforcement Learning (RL) agent builds a compromise between the size of the infected population and the pandemicrelated economic costs.
Identifier
85137178047 (Scopus)
ISBN
[9781713858072]
Publication Title
Iise Annual Conference and Expo 2022
Recommended Citation
Bushaj, Sabah; Büyüktahtakın, Esra; and Beqiri, Arjeta, "Predicting the COVID19 Trajectory with a Simulation Deep Reinforcement Learning Approach" (2022). Faculty Publications. 3361.
https://digitalcommons.njit.edu/fac_pubs/3361
