@inproceedings{569ba0f05b1d4692af748696683c9728,
title = "Optimizing Pharmaceutical and Non-pharmaceutical Interventions During Epidemics",
abstract = "Controlling the spread of infectious diseases is a major challenge. Understanding the dynamics between human behavior and the spread of infection is essential for policymakers. Evolving contagion dynamics make it difficult to develop an efficient mitigation strategy. In this paper, we develop an epidemiological model to forecast the epidemic and use an offline reinforcement learning framework that adapts to the evolving dynamics of disease spread to optimize the mitigation strategy. We demonstrate that our framework can produce efficient mitigation strategies for the COVID-19 pandemic based on data collected from New York, USA.",
keywords = "COVID-19, Epidemiological model, Mitigation regulations, Optimization, Pandemic, Reinforcement learning, SEIHRD model",
author = "Nitin Kulkarni and Chunming Qiao and Alina Vereshchaka",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 15th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation Conference, SBP-BRiMS 2022 ; Conference date: 20-09-2022 Through 23-09-2022",
year = "2022",
doi = "10.1007/978-3-031-17114-7\_22",
language = "English",
isbn = "9783031171130",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "229--240",
editor = "Robert Thomson and Christopher Dancy and Aryn Pyke",
booktitle = "Social, Cultural, and Behavioral Modeling - 15th International Conference, SBP-BRiMS 2022, Proceedings",
address = "Germany",
}