Pharmacological, Non-Pharmacological Policies and Mutation: An Artificial Intelligence Based Multi-Dimensional Policy Making Algorithm for Controlling the Casualties of the Pandemic Diseases

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dc.authoridTutsoy, Onder/0000-0001-6385-3025
dc.contributor.authorTutsoy, Onder
dc.date.accessioned2025-04-09T09:21:16Z
dc.date.available2025-04-09T09:21:16Z
dc.date.issued2022
dc.description.abstractFighting against the pandemic diseases with unique characters requires new sophisticated approaches like the artificial intelligence. This paper develops an artificial intelligence algorithm to produce multi-dimensional policies for controlling and minimizing the pandemic casualties under the limited pharmacological resources. In this respect, a comprehensive parametric model with a priority and age-specific vaccination policy and a variety of non-pharmacological policies are introduced. This parametric model is utilized for constructing an artificial intelligence algorithm by following the exact analogy of the model-based solution. Also, this parametric model is manipulated by the artificial intelligence algorithm to seek for the best multi-dimensional non-pharmacological policies that minimize the future pandemic casualties as desired. The role of the pharmacological and non-pharmacological policies on the uncertain future casualties are extensively addressed on the real data. It is shown that the developed artificial intelligence algorithm is able to produce efficient policies which satisfy the particular optimization targets such as focusing on minimization of the death casualties more than the infected casualties or considering the curfews on the people age over 65 rather than the other non-pharmacological policies. The paper finally analyses a variety of the mutant virus cases and the corresponding non-pharmacological policies aiming to reduce the morbidity and mortality rates.
dc.description.sponsorshipTUBITAK [120M793]
dc.description.sponsorshipThis work was supported by TUBITAK under Grant 120M793.
dc.identifier.doi10.1109/TPAMI.2021.3127674
dc.identifier.endpage9488
dc.identifier.issn0162-8828
dc.identifier.issn1939-3539
dc.identifier.issue12
dc.identifier.pmid34767503
dc.identifier.scopus2-s2.0-85119003256
dc.identifier.scopusqualityQ1
dc.identifier.startpage9477
dc.identifier.urihttps://doi.org/10.1109/TPAMI.2021.3127674
dc.identifier.urihttps://hdl.handle.net/20.500.14669/3575
dc.identifier.volume44
dc.identifier.wosWOS:000880661400065
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherIEEE Computer Soc
dc.relation.ispartofIeee Transactions on Pattern Analysis and Machine Intelligence
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectArtificial intelligence
dc.subjectPandemics
dc.subjectVaccines
dc.subjectParametric statistics
dc.subjectCOVID-19
dc.subjectComputational modeling
dc.subjectOptimization
dc.subjectArtificial intelligence
dc.subjectpandemic
dc.subjectpriority and age specific vaccination policy
dc.subjectparametric model
dc.subjectprediction
dc.subjectnon-pharmacological policies
dc.subjectmutant virus
dc.titlePharmacological, Non-Pharmacological Policies and Mutation: An Artificial Intelligence Based Multi-Dimensional Policy Making Algorithm for Controlling the Casualties of the Pandemic Diseases
dc.typeArticle

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