Data-driven decision making for modelling covid-19 and its implications: A cross-country study

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dc.authoridAtaman, Mustafa Gökalp/0000-0003-4468-0020
dc.authoridsariyer, gorkem/0000-0002-8290-2248
dc.authoridKUMAR MANGLA, SACHIN/0000-0001-7166-5315
dc.authoridKazancoglu, Yigit/0000-0001-9199-671X
dc.authorwosidAtaman, Mustafa Gökalp/O-4644-2017
dc.authorwosidsariyer, gorkem/AAA-1524-2019
dc.authorwosidKazancoglu, Yigit/E-7705-2015
dc.contributor.authorSariyer, Gorkem
dc.contributor.authorMangla, Sachin Kumar
dc.contributor.authorKazancoglu, Yigit
dc.contributor.authorJain, Vranda
dc.contributor.authorAtaman, Mustafa Gokalp
dc.date.accessioned2024-03-09T18:48:29Z
dc.date.available2024-03-09T18:48:29Z
dc.date.issued2023
dc.departmentİzmir Bakırçay Üniversitesien_US
dc.description.abstractGrounded in big data analytics capabilities, this study aims to model the COVID-19 spread globally by considering various factors such as demographic, cultural, health system, economic, technological, and policy-based. Classified values on each country's case, death, and recovery numbers (per 1000,000 population) were used to represent COVID-19 spread. Data sets also included 29 input variables for the corresponding six factors, containing data from 159 countries. The proposed model used a Multilayer Perceptron algorithm. The results show that each of the pre-mentioned factors significantly affects disease spread. Urban population, median age, life expectancy, numbers of medical doctors and nursing personnel, current health expenditure as a % of GDP, international health regulations capacity score, continent, literacy rate, governmental response stringency index, testing policy, internet usage %, human development index and GDP per capita were identified as significant. Taking early measures and adopting open public testing policies were recommended to policymakers in fighting pandemic diseases since the created scenarios on policy-based factors revealed their importance.en_US
dc.identifier.doi10.1016/j.techfore.2023.122886
dc.identifier.issn0040-1625
dc.identifier.issn1873-5509
dc.identifier.scopus2-s2.0-85173017066en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.techfore.2023.122886
dc.identifier.urihttps://hdl.handle.net/20.500.14034/1338
dc.identifier.volume197en_US
dc.identifier.wosWOS:001087114800001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Science Incen_US
dc.relation.ispartofTechnological Forecasting and Social Changeen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBig Data Analytics; Policy-Based Factors; Covid-19; Number Of Cases; Number Of Deathsen_US
dc.titleData-driven decision making for modelling covid-19 and its implications: A cross-country studyen_US
dc.typeArticleen_US

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