Investigation of internet search engine queries of the symptoms related to Covid-19 from Turkey

Year 2021, , 7 - 14, 23.03.2021

M. Fevzi Esen

https://doi.org/10.47582/jompac.870310

Abstract

Objective: In this study, we aimed a retrospective analysis of internet search engine queries related to the symptoms of Covid-19 from Turkey before and during the pandemic period.
Material and Method: The data were obtained using Google Trends. The keywords were “fever, weakness-fatigue, cough, sore throat, muscle pain, diarrhea, headache, back pain, conjunctivitis, low back pain, shortness of breath, loss of taste and smell” among the clinical findings of Covid-19.
Results: The queries related to Covid-19 symptoms reached the highest level in the days following the official announcement of the first case in Turkey. The queries increased by 230% compared to the day before the announcement. The queries increased by approximately 66% compared to the previous day, following the official announcement. A significant difference was found between the frequency of the queries as fever, diarrhea, headache, conjunctivitis, cough, shortness of breath, loss of smell and taste, sore throat, back pain and muscle pain between pre-Covid-19 and Covid-19 periods (p <0,01). There was no significant difference in terms of weakness-fatigue, low back pain symptoms queries (p>0,01).
Conclusion: The frequency of the queries increased significantly in the Covid-19 period compared to the pre-Covid-19 period. In order to predict outbreaks and their effects, the analysis of search engine queries can enable people to be informed in advance about the situations that may occur and to take the necessary measures before the pandemic. This can assist decision-makers in planning health services effectively.

Keywords

Covid-19, internet, symptoms, public health informatics

COVID-19 salgınıyla ilişkili semptomların Türkiye’den gerçekleştirilen internet arama motoru sorgularının incelenmesi

Abstract

Amaç: Bu çalışmada COVID-19 öncesi ve pandemi döneminde COVID-19 semptomlarına ilişkin Türkiye’den gerçekleştirilen internet arama motoru sorgulamalarının retrospektif olarak analiz edilmesi amaçlanmıştır.
Gereç ve Yöntem: Veriler Google Trends uygulaması kullanılarak elde edilmiştir. Kullanılan anahtar kelimeler Ankara Üniversitesi Tıp Fakültesi tarafından hazırlanan COVID-19 kılavuzunda belirtilen klinik bulgulardan “ateş, halsizlik-yorgunluk, öksürük, boğaz ağrısı, kas ağrısı, ishal, baş ağrısı, sırt ağrısı, konjonktivit, bel ağrısı, nefes darlığı, koku ve tat kaybı” alınarak gerçekleştirilmiştir. Türkiye’den gerçekleştirilen COVID-19 sorgularının ani artış gösterdiği 20 Ocak 2020 tarihi ve sonrası “COVID-19 dönemi”, 20 Ocak 2020 ve öncesi dönem ise “COVID-19 öncesi” olarak ikiye ayrılmıştır.
Bulgular: COVID-19 ile ilişkili aramalarda Türkiye’de resmi olarak ilk vakanın duyurulmasını izleyen günlerde en yüksek sorgu seviyelerine ulaşıldı. Resmî açıklamanın yapıldığı gün ilgili sorgular, bir önceki güne göre %230 oranında artmış olup, resmî açıklamayı takip eden günde söz konusu sorguların yaklaşık %66 oranında arttığı görüldü. COVID-19 öncesi ve COVID-19 dönemleri arasında; ateş, ishal, baş ağrısı, konjonktivit, öksürük, nefes darlığı, koku ve tat kaybı, boğaz ağrısı, sırt ağrısı ve kas ağrısı şikayetlerini sorgulama sıklıkları arasında iki dönem arasında anlamlı bir farklılık tespit edildi (p<0,01). Halsizlik- yorgunluk, bel ağrısı semptomları sorgusu açısından anlamlı bir fark bulunamadı (p>0,01).
Tartışma: Kullanılan anahtar kelimelerin aranma sıklığının, COVID-19 döneminde, öncesi döneme göre anlamlı olarak arttığı tespit edildi. Salgınların ve etkilerinin önceden tahminlenmesi amacıyla arama motoru sorgularının analizi, gerekli tedbirlerin alınması ve karar vericilerin sağlık hizmetlerini etkin olarak planlamalarına imkân sağlayabilir.

Keywords

halk sağlığı bilişimi, COVID-19, internet, semptomlar

References

• Hocberg I, Allon R, Yom-Tov E. Assessment of the frequency of online searches for symptoms before diagnosis: analysis of archival data. J Med Internet Res 2020; 22: 1-7.
• Kuehn BM. More than one-third of US individuals use the internet to self-diagnose. JAMA 2013; 309: 756-7.
• Fox S, Duggan M. Pew Research Center’s Internet & American Life Project. Health Online 2013; 1-55.
• Tonsaker T, Bartlett G, Trpkov C. Health information on the Internet: gold mine or minefield?. Can Fam Physician 2014; 60: 407-408.
• Smailhodzic E, Hooijsma W, Boonstra A, LangleyDJ. Social media use in healthcare: A systematic review of effects on patients and on their relationship with healthcare professionals. BMC Health Services Research 2016; 16: 1-14.
• Mosa AS, Yoo I, Sheets L. A systematic review of healthcare applications for smartphones. BMC Med Inform Decis Mak 2012; 1: 12-67.
• Perez J, Poon C, Merrifield R, Wong S,Yang, Guang-Zhong. Big Data for Health. IEEE Journal of Biomedical and Health Informatics 2015; 19: 1193-1208.
• Jacobs W, Amuta AO, Jeon KC. Health information seeking in the digital age: An analysis of health information seeking behavior among US adults. Cogent Social Sciences 2017; 3: 1-11.
• Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature 2009; 457: 1012-14.
• Martin SS, Quaye E, Schultz S, et al. Randomized controlled trial of online symptom searching to inform patient generated differential diagnoses. Digit Med 2019; 2: 1-6.
• Paul MJ, Dredze M. Social Monitoring for Public Health.USA: Morgan & Claypool Publishers; 2017.
• Mackintosh N, Agarwal S, Adcock K ve ark. Online resources and apps to aid self-diagnosis and help seeking in the perinatal period: A descriptive survey of women’s experiences. Midwifery 2020; 90: 1-8.
• Cervellin G, Comelli I, Lippi G. Is Google Trends a reliable tool for digital epidemiology? Insights from different clinical settings. J Epidemiol Global Health 2017; 7: 185-9.
• 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, 2020. Euro Surveill 2020; 25: 1-5.
• Rovetta A, Bhagavathula AS. COVID-19-Related Web Search Behaviors and Infodemic Attitudes in Italy: Infodemiological Study. JMIR Public Health Surveill 2020; 6: 1-8.
• Higgins TS, Wu AW, Sharma D, et al. Correlations of Online Search Engine Trends With Coronavirus Disease (COVID-19) Incidence: Infodemiology Study. JMIR Public Health Surveill 2020; 6: e19702.
• Ciaffi J, Meliconi R, Landini MP, Ursini F. Google Trends and COVID‐19 in Italy: Could we brace for impact?. Internal and Emergency Medicine 2020; 15: 1555-9.
• Effenberger M, Kronbichler A, Shin J, Mayer G, Tilg H, Perco P. Association of the COVID-19 pandemic with internet search volumes: a google trends analysis. Int J Infect Dis 2020; 95: 192-7.
• Çıra G, Birengel MS. Covid-19 Genel Klinik Özellikler. In: Covid 19, Memikoğlu O, Genç V (editors). Ankara Üniversitesi Basımevi; 2020: 43-49.
• Bennett GG, Glasgow RE. The delivery ofpublic health interventions via the internet: Actualizing their potential. Annual review of public health 2009; 30: 273-92.
• HigginsO, SixsmithJ, Barry MM, DomeganC. A literature review on health information seeking behaviour on the web: a health consumer and health professional perspective. ECDC Technical Report Stockholm 2011; 1-12.
• Kurian SJ, Bhatti AUR, Alvi MA ve ark. Correlations Between COVID-19 Cases and Google Trends Data in the United States: A State-by-State Analysis. Mayo Clin Proc 2020; 95: 2370-81.
• Bento AI, Nguyen T, Wing C, Lozano-Rojas F, Ahn Y,Simon K. Evidence from internet search data shows information- seeking responses to news of local Covid-19 cases. PNAS 2020; 117: 11220-2.
• Guan W, Ni Z, Hu Y ve ark.Clinical characteristics of coronavirüs disease 2019 in China. N Engl J Med 2020; 382: 1708-20.
• Bernardo SP, Anto A, Czarlewski W, Anto JM, Fonseca JA, Bousquet J. Assessment of the impact of the media coverage on Covid-19 –Releated Google Trends data: Infodemiology study. J Med Internet Res 2020; 22: e19611.
• Barros JM, Duggan J, Rebholz-Schuhmann D. The application of internet-based sources for public health surveillance (infoveillance): systematic review. J Med Internet Res 2020; 22: e13680.
• Nuti SV, Wayda B, Ranasinghe I, Wang S, Dreyer RP. The use of google trends in health care research: A sistematic review. PLoS One 2014; 9: e109583.