COVID-19: Data-Driven optimal allocation of ventilator supply under uncertainty and risk

dc.contributor.authorYin, Xuechengen
dc.contributor.authorBüyüktahtakın, İ. Esraen
dc.contributor.authorPatel, Bhumi P.en
dc.date.accessioned2025-03-19T19:49:35Zen
dc.date.available2025-03-19T19:49:35Zen
dc.date.issued2021-12-01en
dc.description.abstractThis study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental model to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. The proposed multi-stage stochastic model overviews different scenarios on the number of asymptomatic individuals while optimizing the distribution of resources, such as ventilators, to minimize the total expected number of newly infected and deceased people. The Conditional Value at Risk (CVaR) is also incorporated into the multi-stage mean-risk model to allow for a trade-off between the weighted expected loss due to the outbreak and the expected risks associated with experiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk epidemics-ventilator-logistics model to the case of controlling COVID-19 in highly-impacted counties of New York and New Jersey. We calibrate, validate, and test our model using actual infection, population, and migration data. We also define a new region-based sub-problem and bounds on the problem and then show their computational benefits in terms of the optimality and relaxation gaps. The computational results indicate that short-term migration influences the transmission of the disease significantly. The optimal number of ventilators allocated to each region depends on various factors, including the number of initial infections, disease transmission rates, initial ICU capacity, the population of a geographical location, and the availability of ventilator supply. Our data-driven modeling framework can be adapted to study the disease transmission dynamics and logistics of other similar epidemics and pandemics.en
dc.description.versionAccepted versionen
dc.format.extentPages 255-275en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1016/j.ejor.2021.11.052en
dc.identifier.issn0377-2217en
dc.identifier.issue1en
dc.identifier.orcidBuyuktahtakin Toy, Esra [0000-0001-8928-2638]en
dc.identifier.otherPMC8632406en
dc.identifier.otherS0377-2217(21)01003-1 (PII)en
dc.identifier.urihttps://hdl.handle.net/10919/124889en
dc.identifier.volume304en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urihttps://www.ncbi.nlm.nih.gov/pubmed/34866765en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectCOVID-19en
dc.subjectMean-CVaR multi-stage stochastic mixed-integer programming modelen
dc.subjectOR in health servicesen
dc.subjectPandemic resource and ventilator allocationen
dc.subjectRisk-averse optimizationen
dc.titleCOVID-19: Data-Driven optimal allocation of ventilator supply under uncertainty and risken
dc.title.serialEuropean Journal of Operational Researchen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherJournal Articleen
dcterms.dateAccepted2021-11-26en
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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