Online Public Interest in COVID-19 in Costa Rica Using Google Trends, 2019-2021
Main Article Content
Keywords
COVID-19, Pandemic, Infodemiology, Google Trends, Costa Rica
Abstract
Background: The COVID-19 pandemic has highlighted the importance of information and communication technologies (ICTs) in public health. Infodemiology enables monitoring of public interest, offering valuable tools to implement effective measures. Methods: A retrospective infodemiological study was conducted, analyzing Google Trends data on COVID-19 and related topics (symptoms, diagnosis, treatment, vaccination) in Costa Rica and its seven provinces (San José, Cartago, Heredia, Alajuela, Guanacaste, Puntarenas, Limón) from December 2019 to December 2021. The data were correlated with official COVID-19 case numbers. Results: Public interest peaked on March 22, 2020 (±7 days). The most searched topics were the country’s situation (VBR 100) and preventive measures (VBR 85). The average reaction time was 40 days nationwide and 50 days at the provincial level, while the duration of public interest was 49 and 32 days, respectively. Although no direct correlation was found between search volume and case numbers, a significant inverse correlation (r=-0.51 to -0.75, p<0.05) was identified between public interest and confirmed cases. Conclusions: The findings highlight greater public interest in preventive measures and the country’s situation, with less focus on symptoms and quarantine. The inverse correlation suggests the potential predictive use of online searches to support public health strategies. Public interest showed a marked decline in the last months of 2021.
References
2. Alocución de apertura del Director General de la OMS en la rueda de prensa sobre la COVID-19 celebrada el 11 de marzo de 2020 [Internet]. Organización Mundial de la Salud; 2020 [citado 2021 Oct 18]. Disponible en: https://www.who.int/es/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020
3. Holshue ML, DeBolt C, Lindquist S, Lofy KH, Wiesman J, Bruce H, et al. First case of 2019 novel coronavirus in the United States. N Engl J Med. 2020 Mar 5;382(10):929-36.
4. Brasil confirma el primer caso de coronavirus en América Latina. BBC News Mundo [Internet]. [citado 2021 Sep 19]. Disponible en: https://www.bbc.com/mundo/noticias-america-latina-51641436
5. Rodriguez-Morales AJ, Gallego V, Escalera-Antezana JP, Méndez CA, Zambrano LI, Franco-Paredes C, et al. COVID-19 in Latin America: The implications of the first confirmed case in Brazil. Travel Med Infect Dis. 2020;35:101613.
6. Torres I, Sacoto F. Localising an asset-based COVID-19 response in Ecuador. Lancet. 2020 Apr 25;395(10233):1339.
7. PERÚ NEC. La muerte de 99 personas en un día eleva a 1,223 el número de fallecidos en Brasil por coronavirus [Internet]. El Comercio Perú; 2020 [citado 2021 Oct 12]. Disponible en: https://elcomercio.pe/mundo/latinoamerica/coronavirus-en-brasil-en-vivo-hoy-domingo-12-de-abril-ultimas-noticias-infectados-contagiados-muertos-y-resumen-de-hoy-en-directo-brasil-supera-las-1200-muertes-y-los-22000-casos-de-covid-19-ultimo-minuto-noticia/
8. Badillo D. AMLO y sus polémicas declaraciones sobre el coronavirus [Internet]. El Economista; 2020 [citado 2021 Oct 12]. Disponible en: https://www.eleconomista.com.mx/politica/AMLO-y-sus-polemicas-declaraciones-sobre-el-coronavirus-20200321-0001.html
9. Nicaragua, el único país donde los casos de COVID-19 no aumentan, sino que disminuyen [Internet]. [citado 2021 Oct 12]. Disponible en: https://www.aa.com.tr/es/mundo/nicaragua-el-único-país-donde-los-casos-de-covid-19-no-aumentan-sino-que-disminuyen/1812338
10. Burki T. COVID-19 in Latin America. Lancet Infect Dis. 2020 May;20(5):547-8.
11. Leyva Caballero R, Bonal Ruiz R. Voluntad política e intersectorialidad para el enfrentamiento a la COVID-19 en Cuba. Rev Habanera Cienc Méd [Internet]. 2020 Apr [citado 2021 Oct 13];19(2). Disponible en: http://scielo.sld.cu/scielo.php?script=sci_abstract&pid=S1729-519X2020000200017&lng=es&nrm=iso&tlng=es
12. Ugarte KV. Caso confirmado por COVID-19 en Costa Rica [Internet]. Ministerio de Salud Costa Rica; 2020 [citado 2021 Oct 19]. Disponible en: https://www.ministeriodesalud.go.cr/index.php/centro-de-prensa/noticias/741-noticias-2020/1555-caso-confirmado-por-covid-19-en-costa-rica
13. Ministerio de Salud. Sistema Nacional COVID-19 - Costa Rica [Internet]. [citado 2020 Oct 19]. Disponible en: http://geovision.uned.ac.cr/oges/index.html
14. Aoun L, Lakkis N, Antoun J. Prevalence and outcomes of web-based health information seeking for acute symptoms: Cross-sectional study. J Med Internet Res. 2020 Jan 10;22(1):e15148.
15. Li HOY, Bailey A, Huynh D, Chan J. YouTube as a source of information on COVID-19: A pandemic of misinformation? BMJ Glob Health. 2020 May;5(5):e002604.
16. Galán-Rodas E. Recursos de información en internet sobre COVID-19. Rev Hisp Cienc Salud. 2020 Apr 23;6(1):1-3. Disponible en: https://doi.org/10.56239/rhcs.2020.61.413
17. Brownstein JS, Freifeld CC, Madoff LC. Digital disease detection — Harnessing the web for public health surveillance. N Engl J Med. 2009 May 21;360(21):2153-7.
18. Ministerio de Salud de Costa Rica. Decreto Ejecutivo 42227-MP-S [Internet]. 2020 [citado 2021 Oct 15]. Disponible en: https://www.ministeriodesalud.go.cr/sobre_ministerio/prensa/decretos_cvd/decreto_ejecutivo_42227_mp_s_declaratoria_emergencia_nacional.pdf
19. Seifter A, Schwarzwalder A, Geis K, Aucott J. The utility of "Google Trends" for epidemiological research: Lyme disease as an example. Geospat Health. 2010 May;4(2):135-7.
20. Mavragani A, Ochoa G. Google Trends in infodemiology and infoveillance: Methodology framework. JMIR Public Health Surveill. 2019 May 29;5(2):e13439.
21. Verma M, Kishore K, Kumar M, Sondh AR, Aggarwal G, Kathirvel S. Google search trends predicting disease outbreaks: An analysis from India. Healthc Inform Res. 2018 Oct;24(4):300-8.
22. Eysenbach G. Infodemiology and infoveillance: Framework for an emerging set of public health informatics methods to analyze search, communication, and publication behavior on the internet. J Med Internet Res. 2009 Mar 27;11(1):e11.
23. Eysenbach G. Infodemiology and infoveillance tracking online health information and cyberbehavior for public health. Am J Prev Med. 2011 May;40(5 Suppl 2):S154-8.
24. Zhang Y, Bambrick H, Mengersen K, Tong S, Hu W. Using Google Trends and ambient temperature to predict seasonal influenza outbreaks. Environ Int. 2018 Aug 1;117:284-91.
25. Ortiz-Martínez Y, Garcia-Robledo JE, Vásquez-Castañeda DL, Bonilla-Aldana DK, Rodriguez-Morales AJ. Can Google® Trends predict COVID-19 incidence and help preparedness? The situation in Colombia. Travel Med Infect Dis. 2020;37:101703.
26. 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 Jun;95:221-3.
27. Ayyoubzadeh SM, Zahedi H, Ahmadi M, Niakan Kalhori SR. Predicting COVID-19 incidence through analysis of Google Trends data in Iran: Data mining and deep learning pilot study. JMIR Public Health Surveill. 2020 Apr 14;6(2):e18828.
28. Berlinberg EJ, Deiner MS, Porco TC, Acharya NR. Monitoring interest in herpes zoster vaccination: Analysis of Google search data. JMIR Public Health Surveill. 2018 May 2;4(2):e10180.
29. Bragazzi NL, Mahroum N. Google Trends predicts present and future plague cases during the plague outbreak in Madagascar: Infodemiological study. JMIR Public Health Surveill. 2019 Mar 8;5(1):e13142.
30. Gianfredi V, Bragazzi NL, Mahamid M, Bisharat B, Mahroum N, Amital H, et al. Monitoring public interest toward pertussis outbreaks: An extensive Google Trends-based analysis. Public Health. 2018 Dec;165:9-15.
31. Kandula S, Shaman J. Reappraising the utility of Google Flu Trends. PLoS Comput Biol. 2019 Aug;15(8):e1007258.
32. Wang HW, Chen DR. Economic recession and obesity-related internet search behavior in Taiwan: Analysis of Google Trends data. JMIR Public Health Surveill. 2018 Apr 6;4(2):e37.
33. Watad A, Watad S, Mahroum N, Sharif K, Amital H, Bragazzi NL, et al. Forecasting the West Nile Virus in the United States: An extensive novel data streams-based time series analysis and structural equation modeling of related digital searching behavior. JMIR Public Health Surveill. 2019 Feb 28;5(1):e9176.
Article Sidebar
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
RHCS uses the Creative Commons License 4.0 International (CC BY 4.0). For more information, please visit