Google Trendler İle COVID-19'un Analiz Edilmesi: Türkiye ve Dünya Vaka Örnekleri

Öz

Bu çalışmada 01.01.2020 ile 21.03.2021 tarihini içine alan yaklaşık 14 aylık süre içerisinde büyük veri kaynağı olan Google Trends'i kullanarak COVID-19 pandemisinin eğilimleri ve bu eğilimler ile COVID-19 vakaları arasındaki ilişki düzeylerinin analiz edilmesi amaçlanmıştır. Bu amaçla, öncelikle koronavirüs verileriyle ilişkili arama terimlerine göre oluşturulan Google Trends verilerine bağlı olarak pandeminin seyri hem Türkiye hem de dünya bağlamında ortaya konulmuştur. Ardından ise günlük onaylanmış koronavirüs vakaları ile koronavirüs ilişkili arama terimleri kullanılarak üretilen Google Trends verileri karşılaştırmalı olarak hem dünya hem de Türkiye özelinde incelenmiştir. Son olarak, Türkiye'de ve dünyada Google Trend koronavirüs pandemisi ilişkili alt arama sorgularında öne çıkan arama terimleri ortaya konulmuştur. Türkiye ve dünya vakalarından elde edilen bulgular, Google Trend Hit sayıları ile günlük onaylanmış vaka sayıları trendinin birbiriyle benzerlik göstermediğini ortaya koymuştur. Aynı zamanda Türkiye ve dünya vakalarında Google Trend Hit sayılarının izlediği dalgalı seyrin aksine günlük onaylanmış vaka sayılarının doğrusal bir seyir izlediği gözlenmiştir. Google Trends üzerinden halk sağlığına yönelik arama faaliyeti verileri, COVID-19 pandemisi gibi halk sağlığı krizlerinde gerçek zamanlı, bilgilendirici ve maliyet etkili halk sağlığı politikaları oluşturulmasında ön bulgu olarak kullanılabilir. Bu tür bulgular aynı zamanda istatistiki verilerle ile karşılaştırılarak aralarındaki ilişki ortaya konulabilir.

Anahtar Kelimeler

• Google trends
• COVID-19
• Halk sağlığı
• Pandemi
• Salgın

Analyzing COVID-19 with Google Trends: Turkey and World Case Studies

Abstract

In this study, it is aimed to analyze the trends of COVID-19 pandemic and relationship levels between these trends and COVID-19 cases by using Google Trends as a big data source in about 14 months including the dates between 2020-01- 01 and 2021-03-21. For this purpose, first, the course of the pandemic depending on Google Trends data generated according to the search terms associated coronavirus has been demonstrated both in the context of Turkey and World. At the same time, daily confirmed cases, and Google Trends data produced using the search terms associated coronavirus were studied in a comparative context of the world and in Turkey. Finally, featured search terms in Google Trend coronavirus pandemic- related sub-search queries has been demonstrated in World and Turkey. The findings obtained in Turkey and world cases have indicated that the trend that Google Trends Hit counts followed was not similar to the one that the number of daily confirmed coronavirus cases followed. Public health search activity data via Google Trends can be used as preliminary evidence for real-time, informative and cost-effective public health policies in public health crises such as COVID-19 pandemic. Such findings can also be compared with statistical data and relationship between them can be revealed.

Keywords

• Google trends
• COVID-19
• Public health
• Pandemic
• Outbreak

References

1. 1. DSO COVID-19 Zaman Akışı. (2019). Dünya Sağlık Örgütü (DSÖ).
2. https://www.who.int/news/item/27-04-2020-who- timeline---COVID-19. Ulaşım Tarihi: 14.04.2021.
3. 2. Dünya Sağlık Örgütü (DSÖ). 13 Nisan 2021 Tarihli Durum Raporu
4. https://www.who.int/docs/default-source/coronaviruse /situation reports/20210413_ weekly_epi_update_35. pdf?sfvrsn=ce70cdf5_4&download=true.Ulaşım Tarihi: 14.04.2021.
5. 3. M.J. Khoury, J.P. Ioannidis Big data meets public health Science, 346 (2014), pp. 1054-1055.
6. 4. J. Liu, J. Li, W. Li, J. Wu. Rethinking big data: a review on the data quality and usage issues ISPRS J. Photogramm. Remote Sens., 115 (2016), pp. 134-142,
7. 5. Jun, Seung-Pyo & Yoo, Hyoung Sun & Choi, San, 2018. "Ten years of research change using Google Trends: From the perspective of big data utilizations and applications," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 69-87,
8. 6. Mavragani. A., Ochoa, G., & Tsagarakis, K. P. (2018). Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research Systematic Review Journal of medical Internet research. 20(11), 270, https://dorong 10.2196 jmir 936.

9. 7. Fazeli Dehkordy S, Carlos RC, Hall KS, Dalton VK. Novel data sources for women's health research: mapping breast screening online information seeking through Google trends. Acad Radiol. 2014 Sep;21(9):1172-6. doi: 10.1016/ j.acra.2014.05.005. Epub 2014 Jul 4. PMID: 24998689; PMCID: PMC4399798.
10. 8. Brigo F, Trinka E. Google search behavior for status epilepticus. Epilepsy Behav. 2015 Aug:49:146-9. doi: 10.1016/j.yebeh.2015.02.029. Epub 2015 Apr 11. PMID: 25873438.
11. 9. Schootman M, Toor A, Cavazos-Rehg P, et al. The utility of Google Trends data to examine interest in cancer screening. BMJ Open 2015;5:e006678. doi: 10.1136/ bmjopen-2014-006678.
12. 10. Mavragani, A., Gkillas, K. COVID-19 predictability in the United States using Google Trends time series. Sci Rep 10, 20693 (2020). https://doi.org/10.1038/s41598-020- 77275-9.
13. 11. Google Trends 2021, https://www.google.com/trends.
14. 12. Li C. Chen LJ. Chen X, Zhang M. Pang CP. Chen 11. Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020. Euro Surveill. 2020 Mar;25(10):2000199, DOI: 10,2807/1560-7917.ES.2020.25.10.2000199. PMID: 32183935; PMCID: PMC7078825
15. 13. Effenberger M, Kronbichler A, Shin JI, Mayer G, Tilg H, Perco P. Association of the COVID-19 pandemic with Internet Search Volumes: A Google Trends TM Analysis. Int J Infect Dis. 2020 Jun;95:192-197. doi: 10.1016/j.ijid.2020. 04.033. Epub 2020 Apr 17. PMID: 32305520; PMCID: PMC7162745.
16. 14. Hong YR, Lawrence J, Williams D Jr, Mainous IIIА. Population-Level Interest and Telehealth Capacity of US Hospitals in Response to COVID-19: Cross-Sectional Analysis of Google Search and National Hospital Survey Data. JMIR Public Health Surveill. 2020 Apr 7;6(2):e18961. doi: 10.2196/18961. PMID: 32250963; PMCID: PMC7141249.
17. 15. United Nations Office for the Coordination of Humanitarian Affairs (OCHA), The Humanitarian Data Exchange (HDX). URL: https://data.humdata.org/dataset/ novel-coronavirus-2019-ncov-cases. Access Date: April 11, 2021.
18. 16. Microsoft Corporation. (2018). Microsoft Excel. Retrieved from https://office.microsoft.com/excel.
19. 17. R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R- project.org/
20. 18. Philippe Massicotte and Dirk Eddelbuettel (2020). gtrendsR; Perform and Display Google Trends Queries. R package version 1.4.7. https://CRAN.R-project.org/ package-gtrendsR.
21. 19. H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
22. 20. Jeffrey B. Arnold (2021). ggthemes: Extra Themes, Scales and Geoms for 'ggplot2'. R package version 4.2.4.
23. https://CRAN.R-project.org/package ggthemes