Explosive Behavior in COVID-19 and Policy Responses: Lessons Learned for Public Health Management

dc.authorscopusid8634722600
dc.authorscopusid18433415200
dc.authorscopusid58551541900
dc.authorscopusid57202042382
dc.authorwosidHJY-2489-2023
dc.authorwosidABD-9167-2021
dc.authorwosidADC-1994-2022
dc.authorwosidGGM-4953-2022
dc.contributor.authorGunduz, Lokman
dc.contributor.authorAysan, Ahmet Faruk
dc.contributor.authorBagci, Rifgi Bugra
dc.contributor.authorKarahan, Hatice
dc.contributor.authorBağcı, Rıfgı Buğra
dc.date.accessioned2025-02-28T08:44:45Z
dc.date.available2025-02-28T08:44:45Z
dc.date.issued2024
dc.departmentİşletme ve Yönetim Bilimleri Fakültesi
dc.description.abstractSince the first case of COVID-19 in Turkey, there has been a lingering question as elsewhere in the world: "When will or should the government impose severe restrictions to protect public health?" From a public health perspective, there is value in developing a model to support proactive implementation of social policies. This study aimed to show the benefits of using a novel econometric test (the Generalized Supremum Augmented Dickey-Fuller Test) to detect explosive behavior (bubbles) in Turkey's daily COVID-19 cases and deaths. Results from the analysis demonstrated a link between identified explosive episodes and critical public health decisions, especially in the case of daily new deaths. They also showed a negative relationship between the formation of exuberant behavior during the pandemic and the vaccination rate. Public health policymakers can incorporate this method into their arsenal to evaluate the overall health situation in combating the pandemic and respond accordingly. Furthermore, among the lessons learned from the Turkish experience is the importance of having a coronavirus scientific advisory board in the decision-making process and the ability to promptly implement policy measures.JEL Codes: C22, C58, I10, I18 Understanding COVID-19 Spikes and Public Health Strategies: Key Takeaways for Better ManagementSince the first COVID-19 case in Turkey, a crucial question has been when the government should enforce strict measures to safeguard public health. This study aimed to use an advanced statistical test to identify unusual patterns (bubbles) in Turkey's daily COVID-19 cases and deaths. The results revealed a connection between these patterns and critical health decisions, especially regarding daily new deaths. Interestingly, there was a negative link between excessive behavior during the pandemic and the vaccination rate. This method could help public health policymakers assess the overall situation and respond effectively. The study also highlighted the importance of a scientific advisory board in decision-making and the swift implementation of policy measures based on lessons from the Turkish experience.
dc.identifier.doi10.1177/21582440231224772
dc.identifier.endpage11
dc.identifier.issn2158-2440
dc.identifier.issue1
dc.identifier.orcid0000-0003-4180-4470
dc.identifier.orcid0000-0001-7363-0116
dc.identifier.orcid0000-0001-7273-1046
dc.identifier.orcid0000-0001-5997-5863
dc.identifier.scopus2-s2.0-85186553109
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttps://doi.org/10.1177/21582440231224772
dc.identifier.urihttps://hdl.handle.net/20.500.12436/7354
dc.identifier.volume14
dc.identifier.wos001176653900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBagci, Rifgi Bugra
dc.language.isoen
dc.publisherSage Publications Inc.
dc.relation.ispartofSage Open
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectCOVID-19
dc.subjectBubble
dc.subjectExplosive behavior
dc.subjectPublic health management
dc.subjectPublic policy
dc.subjectInformation processing
dc.subjectTime series
dc.titleExplosive Behavior in COVID-19 and Policy Responses: Lessons Learned for Public Health Management
dc.typeArticle
dspace.entity.typePublication
relation.isAuthorOfPublication071c33ba-c6f7-434f-9ac3-8c3df5149227
relation.isAuthorOfPublication.latestForDiscovery071c33ba-c6f7-434f-9ac3-8c3df5149227

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