Patterns of public interest in infectious diseases in South Korea, five other major countries, and worldwide: A Google trends analysis
Vol 8, Issue 16, 2024
VIEWS - 501 (Abstract)
Abstract
Background: Various studies have demonstrated the usefulness of Google search data for public health-monitoring systems. The aim of this study is to be estimated interest of public in infectious diseases in infectious diseases in South Korea, the five other countries. Methods: We conducted cross-country comparisons for queries related to the H1N1 virus and Middle East respiratory syndrome coronavirus (MERS-CoV). We analyzed queries related to the novel coronavirus disease (COVID-19) from 20 January to 13 April 2020, and performed time-descriptive and correlation analyses on trend patterns. Results: Trends in H1N1, MERS-CoV, and COVID-19 queries in South Korea matched those in the five other countries and worldwide. The relative search volume (RSV) for the MERS-CoV virus increased as the cumulative number of confirmed cases in South Korea increased and decreased significantly as the number of confirmed cases decreased. The volume of COVID-19 queries dramatically increased as South Korea’s confirmed COVID-19 cases grew significantly at the community level. However, RSV remained stable over time. Conclusions: Google Trends provides real-time data based on search patterns related to infectious diseases, allowing for continuous monitoring of public reactions, disease spread, and changes in perceptions or concerns. We can use this information to adjust their strategies of the prevention of epidemics or provide timely updates to the public.
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- Alicino, C., Bragazzi, N. L., Faccio, V., Amicizia. D., Panatto, D., Gasparini, R., Icardi G, Orsi, A. 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): 54. https://doi.org/10.1186/s40249-015-0090-9
- Balkhy, H. H., Abolfotouh, M. A., Al-Hathlool, R. H., Al-Jumah, M. A. Awareness, attitudes, and practices related to the swine influenza pandemic among the Saudi public. BMC Infectious Diseases, 2020; 10(1): 42. https://doi.org/10.1186/1471-2334-10-42
- Bousquet, J., O’Hehir, R. E., Anto, J. M., D’Amato, G., Mösges, R., Hellings, P. W., Van Eerd, M., Sheikh, A. Assessment of thunderstorm-induced asthma using Google Trends. J Allergy Clin Immunol, 2017; 140(3): 891–893. https://doi.org/10.1016/j.jaci.2017.04.042
- Bragazzi, N. L., Alicino, C., Trucchi, C., Paganino, C., Barberis, I., Martini, M., Sticchi, L., Trinka, E., Brigo, F., Ansaldi, F., Icardi, G., Orsi, A. Global reaction to the recent outbreaks of Zika virus: Insights from a Big Data analysis. PloS one, 2017; 12(9): e0185263. https://doi.org/10.1371/journal.pone.0185263
- Budd, J., Miller, B. S., Manning, E. M., Lampos, V., Zhuang, M., Edelstein, M., McKendry, R. A. Digital technologies in the public-health response to COVID-19. Nature medicine, 2020; 26(8): 1183-1192. https://doi.org/10.1038/s41591-020-1011-4
- Cinarka, H., Uysal, M. A., Cifter, A., Niksarlioglu, E. Y., Çarkoğlu, A. The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis. Scientific Reports, 2021; 11(1): 14387. https://doi.org/10.1038/s41598-021-93836-y
- Collinson, S., Khan, K., Heffernan, J. M. The effects of media reports on disease spread and important public health measurements. PLoS One, 2015; 10(11): e0141423. https://doi.org/10.1371/journal.pone.0141423
- Cowper, A. Covid-19: Are we getting the communications right? BMJ 2020;368:m919. https://doi.org/10.1136/bmj.m919
- Eames, K. T., Tilston, N. L., Brooks-Pollock, E., Edmunds, W. J. Measured dynamic social contact patterns explain the spread of H1N1v influenza. PLoS Comput Biol 2012; 8(3): e1002425. https://doi.org/10.1371/journal.pcbi.1002425
- Fagherazzi, G., Goetzinger, C., Rashid, M. A., Aguayo, G. A., Huiart, L. Digital health strategies to fight COVID-19 worldwide: challenges, recommendations, and a call for papers. Journal of Medical Internet Research, 2020; 22(6): e19284. https://doi.org/10.2196/19284
- Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., Brilliant, L. Detecting influenza epidemics using search engine query data. Nature, 2009; 457(7232): 1012–1014. https://doi.org/10.1038/nature07634
- Heymann, D. L., and Shindo, N. COVID-19: What is next for public health? The Lancet 2020; 395(10224): 542–545. https://doi.org/10.1016/s0140-6736(20)30374-3
- Horby, P., Pham, Q. T., Hens, N., Nguyen, T. T., Le, Q. M., Dang, D. T., Nguyen, M. L., Nguyen, T. H., Alexander, N., Edmunds, W. J., Tran, N. D., Fox, A., Nguyen, T. H. Social contact patterns in Vietnam and implications for the control of infectious diseases. PloS One 2011; 6(2): e16965. https://doi.org/10.1371/journal.pone.0016965
- Husain, I., Briggs, B., Lefebvre, C., Cline, D. M., Stopyra, J. P., O’Brien, M. C., Vaithi, R., Gilmore, S., Countryman, C. Fluctuation of public interest in COVID-19 in the United States: retrospective analysis of Google Trends search data. JMIR public health and surveillance, 2020; 6(3): e19969. https://doi.org/10.2196/19969
- Ki, M. Epidemiologic characteristics of early cases with 2019 novel coronavirus (2019-nCoV) disease in Korea. Epidemiol Health, 2020; e2020007. https://doi.org/10.4178/epih.e2020007
- Knipe, D., Evans, H., Sinyor, M., Niederkrotenthaler, T., Gunnell, D., John, A. Tracking online searches for emotional wellbeing concerns and coping strategies in the UK during the COVID-19 pandemic: a Google Trends analysis. Wellcome open research, 2020; 5(220): 220. https://doi.org/10.12688/wellcomeopenres.16147.1
- Kurian, S. J., Alvi, M. A., Ting, H. H., Storlie, C., Wilson, P. M., Shah, N. D., Liu, H., Bydon, M. Correlations between COVID-19 cases and google trends data in the United States: A state-by-state analysis. In Mayo Clinic Proceedings, 2020; 95(11): 2370-2381. https://doi.org/10.1016/j.mayocp.2020.08.022
- Lazer, D., Kennedy, R., King, G., Vespignani, A. The parable of Google Flu: traps in big data analysis. Science, 2014; 343(6176): 1203–1205. https://doi.org/10.1126/science.1248506
- Lee M. Diagnosis for imported cases of emerging and reemerging infectious diseases in Korea. Ewha Med J 2016; 39(2): 3744. https://doi.org/10.12771/emj.2016.39.2.37
- Lee, P.I. and Hsueh, P. R. Emerging threats from zoonotic coronaviruses-from SARS and MERS to 2019-nCoV. J Microbiol Immunol Infect 2020. https://doi.org/10.1016/j.jmii.2020.02.001
- Leung, K., Jit, M., Lau, E. H., Wu, J. T. Social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong. Sci Rep 2017; 7(1): 1–12. https://doi.org/10.1038/s41598-017-08241-1
- Majumder, M. S., Santillana, M., Mekaru, S. R., McGinnis, D. P., Khan, K., Brownstein, J. S. Utilizing nontraditional data sources for near real-time estimation of transmission dynamics during the 2015-2016 Colombian Zika virus disease outbreak. JMIR Public Health Surveil 2016; 2(1): e30. https://doi.org/10.2196/publichealth.5814
- Ming, W. K., Huang, F., Chen, Q., Liang, B., Jiao, A., Liu, T., Wu, H., Akinwunmi, B., Li, J., Liu, G., Zhang, C. J. P., Liu, Q. Understanding health communication through Google Trends and news coverage for COVID-19: multinational study in eight countries. JMIR public health and surveillance, 2021; 7(12): e26644. https://doi.org/10.2196/26644
- Nuti, S. V., Wayda, B., Ranasinghe, I., Wang, S., Dreyer, R. P., Chen, S. I., Murugiah, K. The use of Google Trends in health care research: a systematic review. PloS One 2014;9(10): 1–49. e109583. https://doi.org/10.1371/journal.pone.0109583
- Oboho, I. K., Tomczyk, S. M., Al-Asmari, A. M., Banjar, A. A., Al-Mugti, H., Aloraini, M. S., Alkhaldi, K. Z., Almohammadi, E. L., Alraddadi, B. M., Gerber, S. I., Swerdlow, D. L., Watson, J. T., Madani, T. A. 2014 MERS-CoV Outbreak in Jeddah—A link to health care facilities. New Engl J Med 2015; 372(9): 846–854. https://doi.org/10.1056/nejmoa1408636
- Olson, D. R., Konty, K. J., Paladini, M., Viboud, C., Simonsen, L. Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: A comparative epidemiological study at three geographic scales. PLoS Comp Biol 2013; 9(10). https://doi.org/10.1371/journal.pcbi.1003256
- Riou. J., Althaus, C. L. Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020. Eurosurveillance, 2020; 25(4). https://doi.org/10.2807/1560-7917.es.2020.25.4.2000058
- Saegner, T., Austys, D. Forecasting and surveillance of COVID-19 spread using Google trends: literature review. International journal of environmental research and public health, 2022; 19(19): 12394. https://doi.org/10.3390/ijerph191912394
- Santangelo, O. E., Provenzano, S., Piazza, D., Giordano, D., Calamusa, G., Firenze, A. Digital epidemiology: assessment of measles infection through Google Trends mechanism in Italy. Annali di Igiene, Medicina Preventiva e di Comunita, 2019; 31(4). https://doi:10.7416/ai.2019.2300
- Shariatpanahi, S. P., Jafari, A., Sadeghipour, M., Azadeh-Fard, N., Majidzadeh-A, K., Farahmand, L., Ansari, A. M. Assessing the effectiveness of disease awareness programs: Evidence from Google Trends data for the world awareness dates. Telematics and Informatics 2017; 34(7):904-913. https://doi.org/10.1016/j.tele.2017.03.007
- Shin, S. Y., Seo, D. W., An, J., Kwak, H., Kim, S. H., Gwack, J., Jo, M. W. High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea. Scientific reports, 2016; 6(1): 32920. https://doi.org/10.1038/srep32920
- Towers, S., Afzal, S., Bernal, G., Bliss, N., Brown, S., Espinoza, B., Mass media and the contagion of fear: The case of Ebola in America. PLoS One, 2015;10(6): e0129179. https://doi.org/10.1371/journal.pone.0129179
- Wong, Z. S., Zhou, J., Zhang, Q. Artificial intelligence for infectious disease big data analytics. Infect Dis Health 2019; 24(1): 44–48. https://doi.org/10.1016/j.idh.2018.10.002
- Yoo, W., Choi, D. H., Park, K. The effects of SNS communication: How expressing and receiving information predict MERS-preventive behavioral intentions in South Korea. Comp Human Beh 2016; 62: 34–43. https://doi.org/10.1016/j.chb.2016.03.058
- Yousefinaghani, S., Dara, R., Mubareka, S., Sharif, S. Prediction of COVID-19 waves using social media and Google search: a case study of the US and Canada. Frontiers in public health, 2021; 9: 656635. https://doi.org/10.3389/fpubh.2021.656635
- Zhang, L. Blind spots in fighting the outbreak of coronavirus disease 2019. Explor Res Hypoth Med 2020; 5(1): 6–7. https://doi.org/10.14218/erhm.2020.00012
DOI: https://doi.org/10.24294/jipd9872
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