Big data analytics and COVID-19: investigating the relationship between government policies and cases in Poland, Turkey and South Korea

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dc.authoridsariyer, gorkem/0000-0002-8290-2248
dc.authoridAtaman, Mustafa Gökalp/0000-0003-4468-0020
dc.authoridSOZEN, Mert Erkan/0000-0002-7965-6461
dc.authorwosidsariyer, gorkem/AAA-1524-2019
dc.authorwosidAtaman, Mustafa Gökalp/O-4644-2017
dc.contributor.authorSozen, Mert Erkan
dc.contributor.authorSariyer, Gorkem
dc.contributor.authorAtaman, Mustafa Gökalp
dc.date.accessioned2023-03-22T19:47:21Z
dc.date.available2023-03-22T19:47:21Z
dc.date.issued2022
dc.departmentBelirleneceken_US
dc.description.abstractWe used big data analytics for exploring the relationship between government response policies, human mobility trends and numbers of coronavirus disease 2019 (COVID-19) cases comparatively in Poland, Turkey and South Korea. We collected daily mobility data of retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residential areas. For quantifying the actions taken by governments and making a fairness comparison between these countries, we used stringency index values measured with the `Oxford COVID-19 government response tracker'. For the Turkey case, we also developed a model by implementing the multilayer perceptron algorithm for predicting numbers of cases based on the mobility data. We finally created scenarios based on the descriptive statistics of the mobility data of these countries and generated predictions on the numbers of cases by using the developed model. Based on the descriptive analysis, we pointed out that while Poland and Turkey had relatively closer values and distributions on the study variables, South Korea had more stable data compared to Poland and Turkey. We mainly showed that while the stringency index of the current day was associated with mobility data of the same day, the current day's mobility was associated with the numbers of cases 1 month later. By obtaining 89.3% prediction accuracy, we also concluded that the use of mobility data and implementation of big data analytics technique may enable decision-making in managing uncertain environments created by outbreak situations. We finally proposed implications for policymakers for deciding on the targeted levels of mobility to maintain numbers of cases in a manageable range based on the results of created scenarios.en_US
dc.identifier.doi10.1093/heapol/czab096
dc.identifier.endpage111en_US
dc.identifier.issn0268-1080
dc.identifier.issn1460-2237
dc.identifier.issue1en_US
dc.identifier.pmid34365501en_US
dc.identifier.scopus2-s2.0-85123648638en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage100en_US
dc.identifier.urihttps://doi.org/10.1093/heapol/czab096
dc.identifier.urihttps://hdl.handle.net/20.500.14034/651
dc.identifier.volume37en_US
dc.identifier.wosWOS:000743908300009en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherOxford Univ Pressen_US
dc.relation.journalHealth Policy And Planningen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectStringency indexen_US
dc.subjectCOVID-19en_US
dc.subjectbig data analyticsen_US
dc.subjectmobilityen_US
dc.subjectnumber of casesen_US
dc.subjectPredictionen_US
dc.titleBig data analytics and COVID-19: investigating the relationship between government policies and cases in Poland, Turkey and South Koreaen_US
dc.typeArticleen_US

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