Infodemiological study of coronavirus epidemic using Google Trends in Central Asian Republics of Kazakhstan, Kyrgyzstan, Uzbekistan, Tajikistan

Introduction. The coronavirus infection (COVID-19) pandemic has created a unique opportunity to establish patterns in Internet user activity in connection with a new disease. Objective of the study. To assess the relationship between the interest of Internet users in issues related to COVID-19 in the countries of Central Asia: Kazakhstan, Kyrgyzstan, Uzbekistan and Tajikistan and the dynamics of the epidemic process of this infection according to search queries in Google Trends. Materials and methods. The analysis of queries on the topic “COVID-19” received from Google Trends from 09.01 to 04.10.20 was carried out. Results. An analysis of search activity for queries in Kazakhstan that may be associated with symptoms of COVID-19 showed that only queries containing the keyword “smell”, “loss of smell”, “shortness of breath”, “temperature” have a correlation with the confirmed number of coronavirus infection (r = 0.65 for the query “smell”, r = 0.53 for the queries “loss of smell” and “dyspnea”, r = 0.60 for the query “temperature”). In Kyrgyzstan, when analyzing queries that may be associated with coronavirus infection, correlations were found only for those that contained the keyword “smell” (r = 0.62) and “temperature” (r = 0.53) from 09.01 to 04.10.2020. Correlations (r > 0.65) were found between the dynamics of queries containing the keywords “coronavirus”, “infection” among Internet users in Kazakhstan, Kyrgyzstan and Uzbekistan. When analyzing inquiries related to the diagnosis of COVID-19 in Kazakhstan and Kyrgyzstan, an increased interest of the Internet public in the computed tomography method was revealed, and the peak of interest coincided with the maximum number of confirmed cases of COVID-19 (the correlation coefficient was 0.714). Conclusion. The relationship between Internet inquiries, media reports, and actual incidence rates is multifactorial and requires further study. Nevertheless, the main trends in Internet search queries during a pandemic can serve as an additional component of epidemiological surveillance.

COVID - 19, SARS-CoV - 2 coronavirus, Google Trends, correlation, query

1. 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. Internet Res. 2015; 17 (5): e128. DOI: 10.2196/jmir.3863. https://www.jmir.org/2015/5/e128/

2. Chen Y., Zhang Y., Xu Z., Wang X., Lu J., Hu W. Avian influenza A (H7N 9) and related Internet search query data in China. Sci. Rep. 2019; 9 (1): 10434. DOI: 10.1038/s41598–019–46898-y.

3. 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.2018.1302.05. https://www.cabdirect.org/globalhealth/abstract/20193459604

4. 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.

5. 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.

6. Eysenbach G. SARS and population health technology. J. Med. Intern. Res. 2003; 5 (2): e14. DOI: 10.2196/jmir.5.2.e14.

7. 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.

8. 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.

9. Mavragani A., Ochoa G. Google Trends in Infodemiology and Infoveillance: Methodology Framework. JMIR Public Health Surveill. 2019; 5 (2): e13439. DOI: 10.2196/13439.

10. Момыналиев К. Т., Акимкин В. Г. Анализ динамики запросов Google Trends в России в период пандемии коронавирусной инфекции как инструмент эпидемиологического надзора. Эпидемиол. инфекц. болезни. Актуал. вопр. 2020; 10 (4).

11. 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.

12. Ginsberg J., Mohebbi, M., Patel, R. et al. Detecting influenza epidemics using search engine query data. Nature 2009; 457: 1012–4.

13. Shin S., 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 6, 32920 (2016). https://doi.org/10.1038/srep32920

14. Google Trends. https://trends.google.com/trends/?geo=US

15. 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.

16. 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: 1708–1720. DOI: 10.1056/NEJMoa2002032.

17. Chow EJ, Schwartz NG, Tobolowsky FA, et al. Symptom Screening at Illness Onset of Health Care Personnel With SARS-CoV-2 Infection in King County, Washington. JAMA. 2020; 323 (20): 2087–2089. DOI: 10.1001/jama.2020.6637.

18. World Health Organization. [2020–04–10]. WHO timeline – COVID-19. https://www.who.int/news-room/detail/08–04–2020-who-timeline-covid-19

19. Prevention, diagnosis and treatment of new coronavirus infection COVID-19. (In Russ.). https://static-0.minzdrav.gov.ru/system/attachments/attaches/000/052/548/original/МР_COVID-19_%28v.9%29.pdf?1603730062

20. 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.

21. Walker MD, Sulyok M. Online behavioural patterns for Coronavirus disease 2019 (COVID-19) in the United Kingdom. Epidemiol Infect. 2020 Jun 5; 148: e110. DOI: 10.1017/S0950268820001193. PMID: 32498731. PMCID: PMC 7306408.

22. 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.

23. 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.

24. Walker A., Hopkins C., Surda P. Use of Google Trends to investigate loss-of-smellrelated searches during the COVID-19 outbreak. Int. Forum Allergy Rhinol. 2020; 10 (7): 839–47. DOI: 10.1002/alr.22580

25. 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.