Prediction of the Spread of the COVID-19 Pandemic with Google Searches: An Infodemiological Approach

Yıl 2024, Cilt: 13 Sayı: 2, 358 - 367, 20.05.2024

Betül Şen Yavuz , Tanyeli Güneyligil Kazaz , Ecem Akbeyaz Şivet , Betul Kargul

https://doi.org/10.54617/adoklinikbilimler.1378090

Öz

Aim: In outbreaks, public concern is reflected in search behavior. Examining the health literacy of the population and predicting before the diagnosis of cases may benefit the outbreak management. This study aims to evaluate the association of search behavior with the number of new confirmed cases in the affected countries by the Coronavirus disease 2019 (COVID-19) pandemic. This retrospective study is based on monitoring search behavior with an infodemiology and infoveillance approach.
Materials and Method: Google TrendsTM was used to investigate Internet search behavior related to COVID-19 for 10 countries from February 15, 2020, to November 10, 2020. Spearman’s rank correlation and time-lag correlation were used to determine the correlation with a delay of -30 days to +30 days between public interest and new daily confirmed cases.
Results: The level of COVID-19-related interest peaked about 33 days before the first peak in the number of cases. The correlation gradually decreased in seven countries towards the peak of cases. Spearman's rank correlations between Google searches and the number of new confirmed cases showed a negative correlation in Argentina, Brazil, India, and the United Kingdom (p<0.001), and a positive correlation in Italy, Turkey (p<0.001), and Russia (p=0.017). Eight countries had negative correlations in the increasing phase (p<0.001), and eight countries had strong to moderate positive correlations in the decreasing phase (p<0.001).
Conclusion: The findings showed that searches on Google TrendsTM increased before new cases in the countries.

Anahtar Kelimeler

COVID-19, Health information, Pandemic, Google Trends, Internet, Search interest

Etik Beyan

In the study, ethical approval was not required as anonymous public data is used.

Destekleyen Kurum

NA

Teşekkür

NA

Google Aramaları ile COVID-19 Pandemisinin Yayılımının Tahmini: İnfodemiyolojik Yaklaşım

Öz

Amaç: Salgınlarda, halkın endişesi İnternet arama davranışına yansır. Nüfusun sağlık okuryazarlığının incelenmesi ve vakalara tanı konulmadan önce tahmin yapılması ülkelerin salgın yönetimine fayda sağlayabilir. Bu çalışma Koronavirüs hastalığı 2019 (COVID-19) pandemisinden etkilenen ülkelerdeki doğrulanmış yeni vaka sayısı ile arama davranışı arasındaki ilişkiyi değerlendirmeyi amaçlamaktadır. Bu retrospektif çalışma, arama davranışının bir infodemiyoloji ve bilgi gözetimi yaklaşımıyla izlenmesine dayanmaktadır.
Gereç ve Yöntem: On ülke için 15 Şubat 2020 tarihinden 10 Kasım 2020 tarihine kadar COVID-19 ile ilgili İnternet arama davranışını araştırmak için Google TrendsTM kullanıldı. Halkın ilgisi ile günlük doğrulanmış yeni vakalar arasındaki -30 gün ila +30 gün gecikmeli korelasyonu belirlemek için Spearman rank korelasyonu ve zaman gecikmesi korelasyonu kullanıldı.
Bulgular: COVID-19 ile ilgi düzeyi, vaka sayısındaki ilk zirveden yaklaşık 33 gün önce zirve yaptı. Korelasyon yedi ülkede vakaların zirvesine doğru kademeli olarak azaldı. Google aramaları ile doğrulanmış yeni vaka sayısı arasındaki Spearman sıralama korelasyonları; Arjantin, Brezilya, Hindistan ve Birleşik Krallık'ta negatif bir korelasyon (p<0.001) ve İtalya, Türkiye (p<0.001) ve Rusya'da pozitif bir korelasyon gösterdi. (p=0.017). Sekiz ülke artan fazda negatif korelasyon vardı (p<0.001), sekiz ülke ise azalan fazda güçlü ila orta derecede pozitif korelasyon vardı (p<0.001).
Sonuç: Bulgular, ülkelerdeki yeni vakalardan önce Google TrendsTM'deki aramaların arttığını gösterdi.

Anahtar Kelimeler

COVID-19, Sağlık bilgisi, Pandemi, Google Trends, İnternet, Arama ilgisi

Kaynakça

• Li C, Chen LJ, Chen X, Zhang M, Pang CP, Chen H. Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China. Euro Surveill 2020;25:2000199.
• World Health Organization [Internet]. Pneumonia of unknown cause – China’, Emergencies preparedness, response, Disease outbreak news. [cited 2022 Dec 5]. Available from: https://www.who.int/csr/don/05-january-2020-pneumonia-of-unkown-cause-china/en/.
• Effenberger M, Kronbichler A, Shin JI, Mayer G, Tilg H, Perco P. Association of the COVID 19 pandemic with Internet search volumes: a Google TrendsTM analysis. Int J Infect Dis 2020;95:192–7.
• Hu D, Lou X, Xu Z, Meng N, Xie Q, Zhang M, et al. More effective strategies are required to strengthen public awareness of COVID-19: Evidence from Google Trends. J Glob Health 2020;10:011003.
• World Health Organization [Internet]. WHO Director-general's opening remarks at the media briefing on COVID-19 - 11 March 2020. [cited 2022 Dec 5]. Available from: https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020.
• Lin YS, Liu CH, Chiu YC. Google searches for the keywords of “wash hands” predict the speed of national spread of COVID-19 outbreak among 21 countries. Brain Behav Immun 2020;87:30–2.
• Mavragani A, Ochoa G, Tsagarakis KP. Assessing the methods, tools, and statistical approaches in Google Trends research: systematic review. J Med Internet Res 2018;20:e270.
• Cinelli M, Quattrociocchi W, Galeazzi A, Valensise CM, Brugnoli E, Schmidt AL, et al. The COVID-19 social media infodemic. Sci Rep 2020;10:1–10.
• Statista [Internet]. Internet usage worldwide - Statistics & Facts. [cited 2022 Dec 5]. Available from: https://www.statista.com/topics/1145/internet-usage-worldwide/.
• Arora VS, McKee M, Stuckler D. Google Trends: Opportunities and limitations in health and health policy research. Health Policy 2019;123:338–41.
• Husnayain A, Fuad A, Su ECY. Applications of Google Search Trends for risk communication in infectious disease management: a case study of the COVID-19 outbreak in Taiwan. Int J Infect Dis 2020;95:221–3.
• Walker A, Hopkins C, Surda P. The use of Google Trends to investigate the loss of smell related searches during COVID-19 outbreak. Int Forum Allergy Rhinol 2020;10:839–47.
• Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature 2009;457:1012–4.
• Alicino C, Bragazzi NL, Faccio V, Amicizia D, Panatto D, Gasparini R, et al. Assessing Ebola-related web search behaviour: insights and implications from an analytical study of Google Trends-based query volumes. Infect Dis Poverty 2015;4:1–13.
• Chowell G, Bertozzi SM, Colchero MA, L, Lopez-Gatell H, Alpuche-Aranda C, Hernandez M, et al. Severe respiratory disease concurrent with the circulation of H1N1 influenza. New Engl J Med 2009;361:674–9.
• Mavragani A, Ochoa G. Google Trends in infodemiology and infoveillance: methodology framework. JMIR Public Health Surveill 2019;5:e13439.
• Our World in Data. [Internet]. [cited 2022 Dec 5]. Available from: https://ourworldindata.org/coronavirus.
• Szmuda T, Ali S, Hetzger TV, Rosvall P, Sloniewski P. Are online searches for the novel coronavirus (COVID-19) related to media or epidemiology? A cross-sectional study. Int J Infect Dis 2020;97:386–90.
• Johns Hopkins University [Internet]. The Center for Systems Science and Engineering. [cited 2022 Dec 5]. Available from: https://coronavirus.jhu.edu/map.html.
• Nuti SV, Wayda B, Ranasinghe I, Wang S, Dreyer RP, Chen SI, et al. The use of Google Trends in health care research: a systematic review. PLoS ONE 2014;9:e109583.
• Carneiro HA, Mylonakis E. Google Trends: A web-based tool for real-time surveillance of disease outbreaks. Clin Infect Dis 2009;49:1557–64.
• Sulyok M, Ferenci T, Walker M. Google Trends data and COVID-19 in Europe: correlations and model enhancement are European wide. Transbound Emerg Dis 2020;68:2610–5.
• Rovetta A, Castaldo L. The impact of COVID-19 on Italian web users: a quantitative analysis of regional hygiene interest and emotional response. Cureus 2020;12:e10719.
• Bults M, Beaujean DJMA, Richardus JH, Voeten HACM. Perceptions and behavioral responses of the general public during the 2009 influenza A (H1N1) pandemic: a systematic review. Disaster Med Public Health Prep 2015;9:207–19.
• Zanin GM, Gentile E, Parisi A, Spasiano D. A preliminary evaluation of the public risk perception related to the COVID-19 health emergency in Italy. Int J Environ Res Public Health 2020;17:E3024.
• Chu JTW, Wang MP, Shen C, Viswanath K, Lam TH, Chan SSC. How, when and why people seek health information online: qualitative study in Hong Kong. Interact J Med Res 2017;6:e24.
• Rutten LJF, Blake KD, Greenberg-Worisek AJ, Allen SV, Moser RP, Hesse, BW. Online health information seeking among US adults: measuring progress toward a healthy people 2020 objective. Public Health Rep 2019134:617–25.
• Sousa-Pinto B, Anto A, Czarlewski W, Anto JM, Fonseca JA, Bousquet J. Assessment of the impact of media coverage on COVID-19–related Google Trends data: infodemiology study. J Med Internet Res 2020;22:e19611.
• Bento AI, Nguyen T, Wing C, Lozano-Rojas F, Ahn YY, Simon K. Evidence from Internet search data shows information-seeking responses to news of local COVID-19 cases. PNAS USA 2020;117:11220–2.
• Schnoell J, Besser G, Jank BJ, Bartosik TJ, Parzefall T, Riss D, et al. The association between COVID-19 cases and deaths and web-based public inquiries. Infect Dis 2020;53:1–8.