Big data analytics and Covid-19

Investigating the relationship between government policies and cases in Poland, Turkey and South Korea

Bibliographic Data

ID16762957
AuthorsMert Erkan Sözen (0000-0002-7965-6461, Business Development & Budget Planning Chef, İzmir Metro Company, 2844 Street, No. 5, 35110, İzmir, Turkey), Görkem Sarıyer (0000-0002-8290-2248, Selçuk Yaşar Campus, Üniversite Street, Ağaçlı Yol No: 37-39, 35100 İzmir, Turkey, corresponding author), Mustafa Gökalp Ataman (0000-0003-4468-0020, Department of Emergency Medicine, Izmir Bakırçay University Çiğli Training and Research Hospital, Gazi Mustafa Kemal, Kaynaklar Street, 35665 İzmir, Turkey)
Year2022
Volume37
Issue1
Pages100-111
Publication date2022-01-13
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueHealth Policy and Planning (JOURNAL)
Journal identifiersISSN: 0268-1080 • E-ISSN: 1460-2237
PublisherOxford University Press (PUBLISHER • GB)
DOI10.1093/heapol/czab096
PMID34365501
OpenAlexW3189127727
LanguageEN
Citations received3
References cited27

We 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

Analytics · Big data · Business · Coronavirus disease 2019 (COVID-19) · Data mining · Data science · Descriptive statistics · Disease · Geography · Government (linguistics) · Index (typography) · Political science · Recreation · Statistics · Computer Science · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Human Mobility and Location-Based Analysis · Mathematics · Medicine