Network-Based Epidemic Control Through Optimal Travel and Quarantine Management

[2407.19133] Network-Based Epidemic Control Through Optimal Travel and Quarantine Management Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Systems and Control arXiv:2407.19133 (eess) [Submitted on 27 Jul 2024] Title: Network-Based Epidemic Control Through Optimal Travel and Quarantine Management Authors: Mahtab Talaei , Apostolos I. Rikos , Alex Olshevsky , Laura F. White , Ioannis Ch. Paschalidis View a PDF of the paper titled Network-Based Epidemic Control Through Optimal Travel and Quarantine Management, by Mahtab Talaei and 4 other authors View PDF HTML (experimental) Abstract: Motivated by the swift global transmission of infectious diseases, we present a comprehensive framework for network-based epidemic control. Our aim is to curb epidemics using two different approaches. In the first approach, we introduce an optimization strategy that optimally reduces travel rates. We analyze the convergence of this strategy and show that it hinges on the network structure to minimize infection spread. In the second approach, we expand the classic SIR model by incorporating and optimizing quarantined states to strategically contain the epidemic. We show that this problem reduces to the problem of matrix balancing. We establish a link between optimization constraints and the epidemic's reproduction number, highlighting the relationship between network structure and disease dynamics. We demonstrate that applying augmented primal-dual gradient dynamics to the optimal quarantine problem ensures exponential convergence to the KKT point. We conclude by validating our approaches using simulation studies that leverage public data from counties in the state of Massachusetts. Subjects: Systems and Control (eess.SY) Cite as: arXiv:2407.19133 [eess.SY] (or arXiv:2407.19133v1 [eess.SY] for this version) https://doi.org/10.48550/arXiv.2407.19133 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1109/TCNS.2025.3590383 Focus to learn more DOI(s) linking to related resources Submission history From: Mahtab Talaei [ view email ] [v1] Sat, 27 Jul 2024 00:42:19 UTC (20,243 KB) Full-text links: Access Paper: View a PDF of the paper titled Network-Based Epidemic Control Through Optimal Travel and Quarantine Management, by Mahtab Talaei and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SY < prev | next > new | recent | 2024-07 Change to browse by: cs cs.SY eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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