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Agent-Based Markov Modeling for Improved COVID-19 Mitigation Policies.
Roberto Capobianco, Varun Kompella, James
Ault, Guni Sharon, Stacy
Jong, Spencer Fox, Lauren
Meyers, Peter R. Wurman, and Peter
Stone.
The Journal of Artificial Intelligence Research (JAIR), 71:953–92, August 2021.
Contains
material that was previously published in an AAMAS
2021 paper and a AAAI 2020 Fall
Symposium paper.
Article available from JAIR website.
Simulator
source code.
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{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>.},
}
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