Predicting the COVID19 Trajectory with a Simulation Deep Reinforcement Learning Approach

Sabah Bushaj, Esra Büyüktahtakın, Arjeta Beqiri

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationIISE Annual Conference and Expo 2022
EditorsK. Ellis, W. Ferrell, J. Knapp
PublisherInstitute of Industrial and Systems Engineers, IISE
ISBN (Electronic)9781713858072
StatePublished - 2022
EventIISE Annual Conference and Expo 2022 - Seattle, United States
Duration: May 21 2022May 24 2022

Publication series

NameIISE Annual Conference and Expo 2022


ConferenceIISE Annual Conference and Expo 2022
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering


  • AgentBased Simulation
  • COVID19
  • Deep Reinforcement Learning
  • Epidemic Control Planning
  • SimulationDeep Reinforcement Learning


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