Exploring optimal control of epidemic spread using reinforcement learning

Abstract Pandemic defines the global outbreak of a disease having a high transmission rate. The impact of a pandemic situation can be lessened by restricting the movement of the mass. However, one of its concomitant circumstances is an economic crisis. In this article, we demonstrate what actions an...

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Autores principales:	Abu Quwsar Ohi, M. F. Mridha, Muhammad Mostafa Monowar, Md. Abdul Hamid
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/83a20bbd17fe46fd8e887130154d6a96
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spelling	oai:doaj.org-article:83a20bbd17fe46fd8e887130154d6a962021-12-02T11:57:58ZExploring optimal control of epidemic spread using reinforcement learning10.1038/s41598-020-79147-82045-2322https://doaj.org/article/83a20bbd17fe46fd8e887130154d6a962020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79147-8https://doaj.org/toc/2045-2322Abstract Pandemic defines the global outbreak of a disease having a high transmission rate. The impact of a pandemic situation can be lessened by restricting the movement of the mass. However, one of its concomitant circumstances is an economic crisis. In this article, we demonstrate what actions an agent (trained using reinforcement learning) may take in different possible scenarios of a pandemic depending on the spread of disease and economic factors. To train the agent, we design a virtual pandemic scenario closely related to the present COVID-19 crisis. Then, we apply reinforcement learning, a branch of artificial intelligence, that deals with how an individual (human/machine) should interact on an environment (real/virtual) to achieve the cherished goal. Finally, we demonstrate what optimal actions the agent perform to reduce the spread of disease while considering the economic factors. In our experiment, we let the agent find an optimal solution without providing any prior knowledge. After training, we observed that the agent places a long length lockdown to reduce the first surge of a disease. Furthermore, the agent places a combination of cyclic lockdowns and short length lockdowns to halt the resurgence of the disease. Analyzing the agent’s performed actions, we discover that the agent decides movement restrictions not only based on the number of the infectious population but also considering the reproduction rate of the disease. The estimation and policy of the agent may improve the human-strategy of placing lockdown so that an economic crisis may be avoided while mitigating an infectious disease.Abu Quwsar OhiM. F. MridhaMuhammad Mostafa MonowarMd. Abdul HamidNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-19 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Abu Quwsar Ohi M. F. Mridha Muhammad Mostafa Monowar Md. Abdul Hamid Exploring optimal control of epidemic spread using reinforcement learning
description	Abstract Pandemic defines the global outbreak of a disease having a high transmission rate. The impact of a pandemic situation can be lessened by restricting the movement of the mass. However, one of its concomitant circumstances is an economic crisis. In this article, we demonstrate what actions an agent (trained using reinforcement learning) may take in different possible scenarios of a pandemic depending on the spread of disease and economic factors. To train the agent, we design a virtual pandemic scenario closely related to the present COVID-19 crisis. Then, we apply reinforcement learning, a branch of artificial intelligence, that deals with how an individual (human/machine) should interact on an environment (real/virtual) to achieve the cherished goal. Finally, we demonstrate what optimal actions the agent perform to reduce the spread of disease while considering the economic factors. In our experiment, we let the agent find an optimal solution without providing any prior knowledge. After training, we observed that the agent places a long length lockdown to reduce the first surge of a disease. Furthermore, the agent places a combination of cyclic lockdowns and short length lockdowns to halt the resurgence of the disease. Analyzing the agent’s performed actions, we discover that the agent decides movement restrictions not only based on the number of the infectious population but also considering the reproduction rate of the disease. The estimation and policy of the agent may improve the human-strategy of placing lockdown so that an economic crisis may be avoided while mitigating an infectious disease.
format	article
author	Abu Quwsar Ohi M. F. Mridha Muhammad Mostafa Monowar Md. Abdul Hamid
author_facet	Abu Quwsar Ohi M. F. Mridha Muhammad Mostafa Monowar Md. Abdul Hamid
author_sort	Abu Quwsar Ohi
title	Exploring optimal control of epidemic spread using reinforcement learning
title_short	Exploring optimal control of epidemic spread using reinforcement learning
title_full	Exploring optimal control of epidemic spread using reinforcement learning
title_fullStr	Exploring optimal control of epidemic spread using reinforcement learning
title_full_unstemmed	Exploring optimal control of epidemic spread using reinforcement learning
title_sort	exploring optimal control of epidemic spread using reinforcement learning
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/83a20bbd17fe46fd8e887130154d6a96
work_keys_str_mv	AT abuquwsarohi exploringoptimalcontrolofepidemicspreadusingreinforcementlearning AT mfmridha exploringoptimalcontrolofepidemicspreadusingreinforcementlearning AT muhammadmostafamonowar exploringoptimalcontrolofepidemicspreadusingreinforcementlearning AT mdabdulhamid exploringoptimalcontrolofepidemicspreadusingreinforcementlearning
_version_	1718394759199850496

Exploring optimal control of epidemic spread using reinforcement learning

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