COVID-19: Agent-based simulation-optimization to vaccine center location vaccine allocation problem

dc.contributor.authorYin, Xuechengen
dc.contributor.authorBushaj, Sabahen
dc.contributor.authorYuan, Yueen
dc.contributor.authorBüyüktahtakın, İ. Esraen
dc.date.accessioned2025-03-19T14:53:45Zen
dc.date.available2025-03-19T14:53:45Zen
dc.date.issued2023-08-10en
dc.description.abstractThis article presents an agent-based simulation-optimization modeling and algorithmic framework to determine the optimal vaccine center location and vaccine allocation strategies under budget constraints during an epidemic outbreak. Both simulation and optimization models incorporate population health dynamics, such as susceptible (S), vaccinated (V), infected (I) and recovered (R), while their integrated utilization focuses on the COVID-19 vaccine allocation challenges. We first formulate a dynamic location–allocation Mixed-Integer Programming (MIP) model, which determines the optimal vaccination center locations and vaccines allocated to vaccination centers, pharmacies, and health centers in a multi-period setting in each region over a geographical location. We then extend the agent-based epidemiological simulation model of COVID-19 (Covasim) by adding new vaccination compartments representing people who take the first vaccine shot and the first two shots. The Covasim involves complex disease transmission contact networks, including households, schools, and workplaces, and demographics, such as age-based disease transmission parameters. We combine the extended Covasim with the vaccination center location-allocation MIP model into one single simulation-optimization framework, which works iteratively forward and backward in time to determine the optimal vaccine allocation under varying disease dynamics. The agent-based simulation captures the inherent uncertainty in disease progression and forecasts the refined number of susceptible individuals and infections for the current time period to be used as an input into the optimization. We calibrate, validate, and test our simulation-optimization vaccine allocation model using the COVID-19 data and vaccine distribution case study in New Jersey. The resulting insights support ongoing mass vaccination efforts to mitigate the impact of the pandemic on public health, while the simulation-optimization algorithmic framework could be generalized for other epidemics.en
dc.description.versionAccepted versionen
dc.format.extentPages 699-714en
dc.format.extent16 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1080/24725854.2023.2223246en
dc.identifier.eissn2472-5862en
dc.identifier.issn2472-5854en
dc.identifier.issue7en
dc.identifier.orcidBuyuktahtakin Toy, Esra [0000-0001-8928-2638]en
dc.identifier.urihttps://hdl.handle.net/10919/124885en
dc.identifier.volume56en
dc.language.isoenen
dc.publisherTaylor & Francisen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectAgent-based simulationen
dc.subjectoptimizationen
dc.subjectvaccination center facility locationen
dc.subjectvaccine allocationen
dc.subjectvaccine distributionen
dc.subjectmixed-integer programmingen
dc.subjectCOVID-19en
dc.subjectSIR modelen
dc.subjectepidemiological modelen
dc.subjectsupply chain and logisticsen
dc.titleCOVID-19: Agent-based simulation-optimization to vaccine center location vaccine allocation problemen
dc.title.serialIISE Transactionsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-groupVirginia Techen
pubs.organisational-groupVirginia Tech/Engineeringen
pubs.organisational-groupVirginia Tech/Engineering/Industrial and Systems Engineeringen
pubs.organisational-groupVirginia Tech/All T&R Facultyen
pubs.organisational-groupVirginia Tech/Engineering/COE T&R Facultyen

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