<efrbr:recordSet xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:efrbr="http://vfrbr.info/efrbr/1.1" xmlns:efrbr-work="http://vfrbr.info/efrbr/1.1/work" xmlns:efrbr-expression="http://vfrbr.info/efrbr/1.1/expression" xmlns:efrbr-manifestation="http://vfrbr.info/efrbr/1.1/manifestation" xmlns:efrbr-person="http://vfrbr.info/efrbr/1.1/person" xmlns:efrbr-corporateBody="http://vfrbr.info/efrbr/1.1/corporateBody" xmlns:efrbr-concept="http://vfrbr.info/efrbr/1.1/concept" xmlns:efrbr-structure="http://vfrbr.info/efrbr/1.1/structure" xmlns:efrbr-responsible="http://vfrbr.info/efrbr/1.1/responsible" xmlns:efrbr-subject="http://vfrbr.info/efrbr/1.1/subject" xmlns:efrbr-other="http://vfrbr.info/efrbr/1.1/other" xsi:schemaLocation="http://vfrbr.info/efrbr/1.1 http://vfrbr.info/schemas/1.1/efrbr.xsd"><efrbr:entities><efrbr-work:work identifier="http://purl.tuc.gr/dl/dias/75C4DB88-95FF-47D6-B85D-0630C7E089CB"><efrbr-work:titleOfTheWork>A novel heuristic algorithm for the modeling and risk assessment of the COVID-19 pandemic phenomenon</efrbr-work:titleOfTheWork></efrbr-work:work><efrbr-expression:expression identifier="http://purl.tuc.gr/dl/dias/75C4DB88-95FF-47D6-B85D-0630C7E089CB"><efrbr-expression:titleOfTheExpression>A novel heuristic algorithm for the modeling and risk assessment of the COVID-19 pandemic phenomenon</efrbr-expression:titleOfTheExpression><efrbr-expression:formOfExpression vocabulary="DIAS:TYPES">
            Peer-Reviewed Journal Publication
            Δημοσίευση σε Περιοδικό με Κριτές
         </efrbr-expression:formOfExpression><efrbr-expression:dateOfExpression type="issued">2021-09-27</efrbr-expression:dateOfExpression><efrbr-expression:dateOfExpression type="published">2020</efrbr-expression:dateOfExpression><efrbr-expression:languageOfExpression vocabulary="iso639-1">en</efrbr-expression:languageOfExpression><efrbr-expression:summarizationOfContent>The modeling and risk assessment of a pandemic phenomenon such as COVID-19 is an important and complicated issue in epidemiology, and such an attempt is of great interest for public health decision-making. To this end, in the present study, based on a recent heuristic algorithm proposed by the authors, the time evolution of COVID-19 is investigated for six different countries/states, namely New York, California, USA, Iran, Sweden and UK. The number of COVID-19-related deaths is used to develop the proposed heuristic model as it is believed that the predicted number of daily deaths in each country/state includes information about the quality of the health system in each area, the age distribution of population, geographical and environmental factors as well as other conditions. Based on derived predicted epidemic curves, a new 3D-epidemic surface is proposed to assess the epidemic phenomenon at any time of its evolution. This research highlights the potential of the proposed model as a tool which can assist in the risk assessment of the COVID-19. Mapping its development through 3D-epidemic surface can assist in revealing its dynamic nature as well as differences and similarities among different districts. </efrbr-expression:summarizationOfContent><efrbr-expression:contextForTheExpression>This article belongs to the special issue: Soft computing techniques in materials science and engineering</efrbr-expression:contextForTheExpression><efrbr-expression:useRestrictionsOnTheExpression type="creative-commons">http://creativecommons.org/licenses/by/4.0/</efrbr-expression:useRestrictionsOnTheExpression><efrbr-expression:note type="journal name">Computer Modeling in Engineering &amp; Sciences</efrbr-expression:note><efrbr-expression:note type="journal volume">125</efrbr-expression:note><efrbr-expression:note type="journal number">2</efrbr-expression:note><efrbr-expression:note type="page range">815–828</efrbr-expression:note></efrbr-expression:expression><efrbr-manifestation:manifestation identifier="https://dias.library.tuc.gr/view/90279"><efrbr-manifestation:titleOfTheManifestation>Asteris_et_al_CMES-Comp. Model. Eng. Sci._125(2)_2020.pdf</efrbr-manifestation:titleOfTheManifestation><efrbr-manifestation:publicationDistribution><efrbr-manifestation:placeOfPublicationDistribution type="distribution">Chania [Greece]</efrbr-manifestation:placeOfPublicationDistribution><efrbr-manifestation:publisherDistributor type="distributor">Library of TUC</efrbr-manifestation:publisherDistributor><efrbr-manifestation:dateOfPublicationDistribution>2021-09-22</efrbr-manifestation:dateOfPublicationDistribution></efrbr-manifestation:publicationDistribution><efrbr-manifestation:formOfCarrier>application/pdf</efrbr-manifestation:formOfCarrier><efrbr-manifestation:extentOfTheCarrier>1.4 MB</efrbr-manifestation:extentOfTheCarrier><efrbr-manifestation:accessRestrictionsOnTheManifestation>free</efrbr-manifestation:accessRestrictionsOnTheManifestation></efrbr-manifestation:manifestation><efrbr-person:person identifier="http://viaf.org/viaf/314897647"><efrbr-person:nameOfPerson vocabulary="VIAF">
            Asteris, Panagiotis G., 1964-
         </efrbr-person:nameOfPerson></efrbr-person:person><efrbr-person:person identifier="B1348C2B-F2BC-411E-BD0F-BC05537EBD0B"><efrbr-person:nameOfPerson vocabulary="">
            Douvika Maria G.
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            Karamani Chrysoula A.
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            Skentou Athanasia D.
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            Chlichlia, Aikaterini
         </efrbr-person:nameOfPerson></efrbr-person:person><efrbr-person:person identifier="15D3E50D-284A-468D-BA1E-89AF77A1FB23"><efrbr-person:nameOfPerson vocabulary="">
            Cavaleri Liborio
         </efrbr-person:nameOfPerson></efrbr-person:person><efrbr-person:person identifier="http://users.isc.tuc.gr/~tdaras"><efrbr-person:nameOfPerson vocabulary="TUC:LDAP">
            Daras Tryfonas
            Δαρας Τρυφωνας
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            Armaghani Danial J.
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            Zaoutis, Theoklis E
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            Tech Science Press
         </efrbr-corporateBody:nameOfTheCorporateBody></efrbr-corporateBody:corporateBody><efrbr-concept:concept identifier="997B2BAB-EF88-45D5-B01F-1C0D025A2A79"><efrbr-concept:termForTheConcept>
            Algorithm
         </efrbr-concept:termForTheConcept></efrbr-concept:concept><efrbr-concept:concept identifier="C87679F8-1D64-4BAF-AF1D-896AE80051C0"><efrbr-concept:termForTheConcept>
            COVID-19
         </efrbr-concept:termForTheConcept></efrbr-concept:concept><efrbr-concept:concept identifier="53B28628-5B47-4886-A935-B814E532B016"><efrbr-concept:termForTheConcept>
            Gaussian-function
         </efrbr-concept:termForTheConcept></efrbr-concept:concept><efrbr-concept:concept identifier="7B230272-DA21-4C02-B7A1-080215197BDB"><efrbr-concept:termForTheConcept>
            Heuristic model
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            Pandemic trend
         </efrbr-concept:termForTheConcept></efrbr-concept:concept><efrbr-concept:concept identifier="CB14C0F8-FA87-463F-941A-1E86638A8B3E"><efrbr-concept:termForTheConcept>
            Prediction
         </efrbr-concept:termForTheConcept></efrbr-concept:concept><efrbr-concept:concept identifier="137DD1D2-91CD-4F9A-A6CB-BE4E4067BED8"><efrbr-concept:termForTheConcept>
            SARS-CoV-2
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