A variant-informed decision support system for tackling COVID-19: a transfer learning and multi-attribute decision-making approach

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dc.authorscopusidArdavan Babaei / 57193898673
dc.authorscopusidErfan Babaee Tirkolaee / 57196032874
dc.authorwosidArdavan Babaei / JLG-3040-2023
dc.authorwosidErfan Babaee Tirkolaee / U-3676-2017
dc.contributor.authorAmiri, Amirreza Salehi
dc.contributor.authorBabaei, Ardavan
dc.contributor.authorSimic, Vladimir
dc.contributor.authorTirkolaee, Erfan Babaee
dc.date.accessioned2025-05-09T10:12:41Z
dc.date.available2025-05-09T10:12:41Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümü
dc.description.abstractThe global impact of the COVID-19 pandemic, characterized by its extensive societal, economic, and environmental challenges, escalated with the emergence of variants of concern (VOCs) in 2020. Governments, grappling with the unpredictable evolution of VOCs, faced the need for agile decision support systems to safeguard nations effectively. This article introduces the Variant-Informed Decision Support System (VIDSS), designed to dynamically adapt to each variant of concern’s unique characteristics. Utilizing multi-attribute decision-making (MADM) techniques, VIDSS assesses a country’s performance by considering improvements relative to its past state and comparing it with others. The study incorporates transfer learning, leveraging insights from forecast models of previous VOCs to enhance predictions for future variants. This proactive approach harnesses historical data, contributing to more accurate forecasting amid evolving COVID-19 challenges. Results reveal that the VIDSS framework, through rigorous K-fold cross-validation, achieves robust predictive accuracy, with neural network models significantly benefiting from transfer learning. The proposed hybrid MADM approach integrated approaches yield insightful scores for each country, highlighting positive and negative criteria influencing COVID-19 spread. Additionally, feature importance, illustrated through SHAP plots, varies across variants, underscoring the evolving nature of the pandemic. Notably, vaccination rates, intensive care unit (ICU) patient numbers, and weekly hospital admissions consistently emerge as critical features, guiding effective pandemic responses. These findings demonstrate that leveraging past VOC data significantly improves future variant predictions, offering valuable insights for policymakers to optimize strategies and allocate resources effectively. VIDSS thus stands as a pivotal tool in navigating the complexities of COVID-19, providing dynamic, data-driven decision support in a continually evolving landscape. Copyright 2024 Salehi Amiri et al. Distributed under Creative Commons CC-BY 4.0
dc.identifier.citationAmiri, A. S., Babaei, A., Simic, V., & Tirkolaee, E. B. (2024). A variant-informed decision support system for tackling COVID-19: a transfer learning and multi-attribute decision-making approach. PeerJ Computer Science, 10, e2321.
dc.identifier.doi10.7717/peerj-cs.2321
dc.identifier.issn23765992
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.7717/peerj-cs.2321
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7248
dc.identifier.volume10
dc.identifier.wosWOS:001322561200004
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorBabaei, Ardavan
dc.institutionauthorTirkolaee, Erfan Babaee
dc.institutionauthoridArdavan Babaei / 0000-0002-3657-4853
dc.institutionauthoridErfan Babaee Tirkolaee / 0000-0003-1664-9210
dc.language.isoen
dc.publisherPeerJ Inc.
dc.relation.ispartofPeerJ Computer Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectCountry Policies
dc.subjectCOVID-19
dc.subjectDecision Support System
dc.subjectMachine Learning
dc.subjectTransfer Learning
dc.titleA variant-informed decision support system for tackling COVID-19: a transfer learning and multi-attribute decision-making approach
dc.typeArticle

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