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@article{JAIR21-covid,
  author = {Roberto Capobianco and Varun Kompella and James Ault and Guni Sharon and Stacy Jong and Spencer Fox and Lauren Meyers and Peter R.\ Wurman and Peter Stone},
  title = {Agent-Based Markov Modeling for Improved {COVID}-19 Mitigation Policies},
  journal={The Journal of Artificial Intelligence Research (JAIR)},
  volume={71},
  year = {2021},
  pages={953--92},
  month={August},
  abstract = {
              The year 2020 saw the covid-19 virus lead to one of the
              worst global pandemics in history. As a result,
              governments around the world have been faced with the
              challenge of protecting public health while keeping the
              economy running to the greatest extent
              possible. Epidemiological models provide insight into
              the spread of these types of diseases and predict the
              effects of possible intervention policies. However, to
              date, even the most data-driven intervention policies
              rely on heuristics. In this paper, we study how
              reinforcement learning (RL) and Bayesian inference can
              be used to optimize mitigation policies that minimize
              economic impact without overwhelming hospital
              capacity. Our main contributions are (1) a novel
              agent-based pandemic simulator which, unlike traditional
              models, is able to model fine-grained interactions among
              people at specific locations in a community; (2) an
              RLbased methodology for optimizing fine-grained
              mitigation policies within this simulator; and (3) a
              Hidden Markov Model for predicting infected individuals
              based on partial observations regarding test results,
              presence of symptoms, and past physical contacts.
  },
Article available from <a href="https://www.jair.org/index.php/jair/article/view/12632"> JAIR website</a>.<br>
Simulator <a href="https://github.com/SonyAI/PandemicSimulator">source code</a>.},
}