Decision-dependent distributionally robust Markov decision process method in dynamic epidemic control.
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| Title: | Decision-dependent distributionally robust Markov decision process method in dynamic epidemic control. |
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| Authors: | Song, Jun1 (AUTHOR), Yang, William1 (AUTHOR), Zhao, Chaoyue1 (AUTHOR) cyzhao@uw.edu |
| Source: | IISE Transactions. Apr2024, Vol. 56 Issue 4, p458-470. 13p. |
| Subjects: | Markov processes, Real-time programming, Epidemics, Dynamic programming, Infectious disease transmission, Computational neuroscience |
| Abstract: | In this article, we present a Distributionally Robust Markov Decision Process (DRMDP) approach for addressing the dynamic epidemic control problem. The Susceptible-Exposed-Infectious-Recovered (SEIR) model is widely used to represent the stochastic spread of infectious diseases, such as COVID-19. Although the Markov Decision Process (MDP) offers a mathematical framework for identifying optimal actions, such as vaccination and transmission-reducing intervention, to combat disease spread calculated using the SEIR model. However, uncertainties in these scenarios demand a more robust approach that is less reliant on error-prone assumptions. The primary objective of our study is to introduce a new DRMDP framework that allows for an ambiguous distribution of transition dynamics. Specifically, we consider the worst-case distribution of these transition probabilities within a decision-dependent ambiguity set. To overcome the computational complexities associated with policy determination, we propose an efficient Real-Time Dynamic Programming (RTDP) algorithm that is capable of computing optimal policies based on the reformulated DRMDP model in an accurate, timely, and scalable manner. Comparative analysis against the classic MDP model demonstrates that the DRMDP achieves a lower proportion of infections and susceptibilities at a reduced cost. [ABSTRACT FROM AUTHOR] |
| Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 174973699 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Decision-dependent distributionally robust Markov decision process method in dynamic epidemic control. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Song%2C+Jun%22">Song, Jun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+William%22">Yang, William</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Chaoyue%22">Zhao, Chaoyue</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> cyzhao@uw.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IISE+Transactions%22">IISE Transactions</searchLink>. Apr2024, Vol. 56 Issue 4, p458-470. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+programming%22">Real-time programming</searchLink><br /><searchLink fieldCode="DE" term="%22Epidemics%22">Epidemics</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamic+programming%22">Dynamic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Infectious+disease+transmission%22">Infectious disease transmission</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+neuroscience%22">Computational neuroscience</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this article, we present a Distributionally Robust Markov Decision Process (DRMDP) approach for addressing the dynamic epidemic control problem. The Susceptible-Exposed-Infectious-Recovered (SEIR) model is widely used to represent the stochastic spread of infectious diseases, such as COVID-19. Although the Markov Decision Process (MDP) offers a mathematical framework for identifying optimal actions, such as vaccination and transmission-reducing intervention, to combat disease spread calculated using the SEIR model. However, uncertainties in these scenarios demand a more robust approach that is less reliant on error-prone assumptions. The primary objective of our study is to introduce a new DRMDP framework that allows for an ambiguous distribution of transition dynamics. Specifically, we consider the worst-case distribution of these transition probabilities within a decision-dependent ambiguity set. To overcome the computational complexities associated with policy determination, we propose an efficient Real-Time Dynamic Programming (RTDP) algorithm that is capable of computing optimal policies based on the reformulated DRMDP model in an accurate, timely, and scalable manner. Comparative analysis against the classic MDP model demonstrates that the DRMDP achieves a lower proportion of infections and susceptibilities at a reduced cost. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/24725854.2023.2219281 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 458 Subjects: – SubjectFull: Markov processes Type: general – SubjectFull: Real-time programming Type: general – SubjectFull: Epidemics Type: general – SubjectFull: Dynamic programming Type: general – SubjectFull: Infectious disease transmission Type: general – SubjectFull: Computational neuroscience Type: general Titles: – TitleFull: Decision-dependent distributionally robust Markov decision process method in dynamic epidemic control. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Song, Jun – PersonEntity: Name: NameFull: Yang, William – PersonEntity: Name: NameFull: Zhao, Chaoyue IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 24725854 Numbering: – Type: volume Value: 56 – Type: issue Value: 4 Titles: – TitleFull: IISE Transactions Type: main |
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