Analysis of Google Trends queries in Russia during the coronavirus infection pandemic as a tool for epidemiological surveillance
- Authors: Momynaliev K.T.1, Akimkin V.G.1
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Affiliations:
- Central Research Institute of Epidemiology Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being
- Issue: Vol 10, No 4 (2020)
- Pages: 33-37
- Section: Articles
- URL: https://journals.eco-vector.com/2226-6976/article/view/287609
- DOI: https://doi.org/10.18565/epidem.2020.10.4.33-7
- ID: 287609
Cite item
Abstract
Central Research Institute of Epidemiology, Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being, Moscow, Russia The coronavirus infection (COVID-19) pandemic has created a unique opportunity to study the activity patterns of Internet users due to the novel disease and to investigate how they are related to the real cases of SARS-CoV-2 infection. Objective. To conduct epidemiological and social studies of public awareness about the novel coronavirus infection (COVID-19) in the Russian Federation. Materials and methods. The «COVID-19» queries obtained using the Google Trends in the period from January 9, 2001 to September 24, 2020 were analyzed. Results. Search activity for the queries that may be associated with COVID-19 symptoms, such as olfaction and loss of olfaction, has a strong positive correlation with the confirmed number of cases in Russia (r = 0.81 for an olfaction query and r = 0.79 for a loss of olfaction query). There was a strong and moderate negative correlation between the queries «cough» and «sputum»: - 0.72 and - 0.53, respectively. A strong positive correlation was also found between the real cases of the infection and the queries related to the diagnosis of COVID-19, such as CT(r = 0.71) and antibodies (r = 0.79). Conclusion. The relationship between relative search volume (RSV) in the Internet and the confirmed number of cases can be of great importance for monitoring the rapidly evolving epidemic situation that requires up-to-date information on the spread of the disease.
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About the authors
Kuvat T. Momynaliev
Central Research Institute of Epidemiology Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being
Email: dhoroshun@gmail.com
D, Associate Professor, Leading Researcher, Central Research Institute of Epidemiology Moscow, Russia
Vasiliy G. Akimkin
Central Research Institute of Epidemiology Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being
Email: vgakimkin@yandex.ru
MD, Academician of the Russian Academy of Sciences, Director, Central Research Institute of Epidemiology Moscow, Russia
References
- Mollema L., Harmsen I.A., Broekhuizen E., Clijnk R., De Melker H., Paulussen T. Et al. Disease detection or public opinion reflection? Content analysis of tweets, other social media, and online newspapers during the measles outbreak in The Netherlands in 2013. J. Med. Intern. Res. 2015; 17(5): e128. doi: 10.2196/jmir.3863. https://www.jmir.org/2 015/5/e128/
- Chen Y., Zhang Y., Xu Z., Wang X., Lu J., Hu W. Avian influenza A (H7N9) and related Internet search query data in China. Sci. Rep. 2019; 9(1): 10434. doi: 10.1038/s41598-019-46898-y
- Mohamed N.A. Knowledge, attitude and practice on bats-borne diseases among village residents: a pilot study. Med & Health 2018; 13(2): 48-57. Doi: 10. 17576/MH. https://www.cabdirect.org/globalhealth/abstract/20193459604
- Zeraatkar K., Ahmadi M. Trends of infodemiology studies: a scoping review. Health Info Libr. J. 2018; 35(2): 91-120. doi: 10.1111/hir.12216
- Tang L., Bie B., Park S., Zhi D. Social media and outbreaks of emerging infectious diseases: A systematic review of literature. Am. J. Infect. Control. 2018; 46(9): 962-72. doi: 10.1016/j.ajic.2018.02.010
- Eysenbach G. SARS and population health technology. J. Med. Intern. Res. 2003; 5(2): e14. doi: 10.2196/jmir.5.2.e14
- Mavragani A., Ochoa G. Infoveillance of infectious diseases in USA: STDs, tuberculosis, and hepatitis. J. Big. Data 2018; 5(1). doi: 10.1186/s40537-018-0140-9
- Roccetti M., Marfia G., Salomoni P., Prandi C., Zagari R.M., Gningaye Kengni F.L. et al. Attitudes of Crohn’s Disease Patients: Infodemiology Case Study and Sentiment Analysis of Facebook and Twitter Posts. JMIR Public Health Surveill. 2017; 3(3): e51. doi: 10.2196/publichealth.7004
- Mavragani A., Ochoa G. Google Trends in Infodemiology and Infoveillance: Methodology Framework. JMIR Public Health Surveill. 2019; 5(2): e13439. doi: 10.2196/13439
- Mavragani A., Ochoa G. Tsagarakis KP (2018) Assessing the methods, tools, and statistical approaches in Google Trends research: systematic review. J. Med. Intern. Res. 2018; 20(11): e270.
- Ginsberg J., Mohebbi M., Patel R. et al. Detecting influenza epidemics using search engine query data. Nature 2009; 457: 1012-4. ЭПИДЕМИОЛОГИЯ И ИНФЕКЦИОННЫЕ БОЛЕЗНИ. АКТУАЛЬНЫЕ ВОПРОСЫ № 4 /2020 EPIDEMIOLOGY AND INFECTIOUS DISEASES. CURRENT ISSUES № 4 /2020 ÈPIDEMIOLOGIÂ I INFEKCIONNYE BOLEZNI. AKTUAL'NYE VOPROSY № 4 /2020 ОРИГИНАЛЬНЫЕ ИССЛЕДОВАНИЯ
- Shin S.Y., Seo D., An J. et al. High correlation of Middle East respiratory syndrome spread with Google search and Twitt.er trends in Korea. Sci. Rep. 2016; 6(2): 32920.
- Google Trends. https://trends.google.com/trends/?geo=US
- Wang C., Horby P.W., Hayden F.G., Gao G.F. A novel coronavirus outbreak of global health concern. Lancet 2020; 395(10223): 470-3. doi: 10.1016/s0140-6736(20)30185-9
- Guan W., Ni Z., Hu Y., Liang W., Ou C., He J. et al. China Medical Treatment Expert Group for Covid-19 Clinical characteristics of coronavirus disease 2019 in China. N. Engl. J. Med. 2020; 382(18): 1708-20. doi: 10.1056/NEJMoa2002032
- Chow E.J., Schwartz N.G., Tobolowsky F.A., Zacks R.L.T., Huntington-Frazier M., Reddy S.C., Rao A.K. Symptom screening at illness onset of health care personnel with SARS-CoV-2 infection in King County, Washington. JAMA 2020; 323(20): 2087-9. doi: 10.1001/jama.2020.6637
- World Health Organization. [2020-04-10]. WHO timeline - COVID-19. https://www.who.int/news-room/detail/08-04-2020-who-timeline-covid-19
- Временные методические рекомендации «Профилактика, диагностика и лечение новой коронавирусной инфекции (COVID-19)». https://static-0.rosminzdrav.ru/system/attachments/attaches/000/050/584/original/03062020_MR_ COVID-19_v7.pdf
- Ayyoubzadeh S.M., Zahedi H., Ahmadi M. Predicting COVID-19 incidence using Google Trends and data mining techniques: a pilot study in Iran. JMIR Public Health Surveill. 2020; 6(2): e18828
- Walker M.D., Sulyok M. Online behavioural patterns for Coronavirus disease 2019 (COVID-19) in the United Kingdom. Epidemiology and Infection 2020; 148: e110. doi: 10.1017/S0950268820001193
- Rovetta A., Bhagavathula A. COVID-19-Related Web Search Behaviors and Infodemic Attitudes in Italy: Infodemiological Study. JMIR Public Health Surveill. 2020; 6(2): e19374. doi: 10.2196/19374
- Effenberger M., Kronbichler A., Shin J.I., 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; 95: 192-7. doi: 10.1016/j.ijid.2020.04.033
- Hu D., Lou X., Xu Z. et al. More effective strategies are required to strengthen public awareness of COVID-19: Evidence from Google Trends. J. Glob. Health 2020; 10(1): 011003. doi: 10.7189/jogh.10.011003
- Higgins T.S., Wu A.W., Sharma D. et al. Correlations of Online Search Engine Trends With Coronavirus Disease (COVID-19) Incidence: Infodemiology Study. JMIR Public Health Surveill. 2020; 6(2): e19702. doi: 10.2196/19702
- Walker A., Hopkins C., Surda P. Use of Google Trends to investigate loss-of-smell-related searches during the COVID-19 outbreak. Int. Forum Allergy Rhinol. 2020; 10(7): 839-47. doi: 10.1002/alr.22580