Published on in Vol 22, No 9 (2020): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19788, first published .
Understanding the Community Risk Perceptions of the COVID-19 Outbreak in South Korea: Infodemiology Study

Understanding the Community Risk Perceptions of the COVID-19 Outbreak in South Korea: Infodemiology Study

Understanding the Community Risk Perceptions of the COVID-19 Outbreak in South Korea: Infodemiology Study

Journals

  1. Atchison C, Bowman L, Vrinten C, Redd R, Pristerà P, Eaton J, Ward H. Early perceptions and behavioural responses during the COVID-19 pandemic: a cross-sectional survey of UK adults. BMJ Open 2021;11(1):e043577 View
  2. Bowman L, Kwok K, Redd R, Yi Y, Ward H, Wei W, Atchison C, Wong S. Comparing Public Perceptions and Preventive Behaviors During the Early Phase of the COVID-19 Pandemic in Hong Kong and the United Kingdom: Cross-sectional Survey Study. Journal of Medical Internet Research 2021;23(3):e23231 View
  3. Banerjee D, Meena K. RETRACTED: COVID-19 as an “Infodemic” in Public Health: Critical Role of the Social Media. Frontiers in Public Health 2021;9 View
  4. Álvarez-Mon M, Rodríguez-Quiroga A, de Anta L, Quintero J. Aplicaciones médicas de las redes sociales. Aspectos específicos de la pandemia de la COVID-19. Medicine - Programa de Formación Médica Continuada Acreditado 2020;13(23):1305 View
  5. Rovetta A. Reliability of Google Trends: Analysis of the Limits and Potential of Web Infoveillance During COVID-19 Pandemic and for Future Research. Frontiers in Research Metrics and Analytics 2021;6 View
  6. Rotter D, Doebler P, Schmitz F. Interests, Motives, and Psychological Burdens in Times of Crisis and Lockdown: Google Trends Analysis to Inform Policy Makers. Journal of Medical Internet Research 2021;23(6):e26385 View
  7. Franch‐Pardo I, Desjardins M, Barea‐Navarro I, Cerdà A. A review of GIS methodologies to analyze the dynamics of COVID‐19 in the second half of 2020. Transactions in GIS 2021;25(5):2191 View
  8. Husnayain A, Chuang T, Fuad A, Su E. High variability in model performance of Google relative search volumes in spatially clustered COVID-19 areas of the USA. International Journal of Infectious Diseases 2021;109:269 View
  9. Kim S, Lim S, Kim S. Real-time analysis and predictability of the health functional food market using big data. Food Science and Biotechnology 2021;30(13):1667 View
  10. Guo H, Yin Q, Xia C, Dehmer M. Impact of information diffusion on epidemic spreading in partially mapping two-layered time-varying networks. Nonlinear Dynamics 2021;105(4):3819 View
  11. Montesi M. Human information behavior during the Covid-19 health crisis. A literature review. Library & Information Science Research 2021;43(4):101122 View
  12. 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 View
  13. Kłak A, Grygielska J, Mańczak M, Ejchman-Pac E, Owoc J, Religioni U, Olszewski R. Online Information of COVID-19: Visibility and Characterization of Highest Positioned Websites by Google between March and April 2020—A Cross-Country Analysis. International Journal of Environmental Research and Public Health 2022;19(3):1491 View
  14. An L, Russell D, Mihalcea R, Bacon E, Huffman S, Resnicow K. Online Search Behavior Related to COVID-19 Vaccines: Infodemiology Study. JMIR Infodemiology 2021;1(1):e32127 View
  15. Moon H, Lee G, Cho Y. Readability of Korean-Language COVID-19 Information from the South Korean National COVID-19 Portal Intended for the General Public: Cross-sectional Infodemiology Study. JMIR Formative Research 2022;6(3):e30085 View
  16. Husnayain A, Shim E, Fuad A, Su E. Predicting New Daily COVID-19 Cases and Deaths Using Search Engine Query Data in South Korea From 2020 to 2021: Infodemiology Study. Journal of Medical Internet Research 2021;23(12):e34178 View
  17. Htay M, Parial L, Tolabing M, Dadaczynski K, Okan O, Leung A, Su T, Padhi B. Digital health literacy, online information-seeking behaviour, and satisfaction of Covid-19 information among the university students of East and South-East Asia. PLOS ONE 2022;17(4):e0266276 View
  18. Lee J. The Gendered Outbreak of COVID-19 in South Korea. Feminist Economics 2022;28(4):89 View
  19. Montesi M. Everyday information behavior during the “new normal” of the Covid-19 pandemic: approaching the notions of experiential and local knowledge. Journal of Documentation 2023;79(1):160 View
  20. Shin Y, Seo H, Lee S, Jang Y, Kim H. South Korean government’s risk communication during the COVID-19 pandemic crisis: Lessons learned and policy recommendations. Korean Journal of Health Education and Promotion 2021;38(4):63 View
  21. Hwang J. Subjective Changes in Tobacco Product Use among Korean Adults during the COVID-19 Pandemic. International Journal of Environmental Research and Public Health 2022;19(6):3272 View
  22. Ming W, Huang F, Chen Q, Liang B, Jiao A, Liu T, Wu H, Akinwunmi B, Li J, Liu G, Zhang C, Huang J, 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 View
  23. Kim C, Kim Y, Heo N, Park E, Choi S, Jang S, Kim N, Kwon D, Park Y, Choi B, Ha B, Jung K, Park C, Park S, Lee H. COVID-19 outbreak in a military unit in Korea. Epidemiology and Health 2021;43:e2021065 View
  24. Chekar C, Kim H. COVID-19 Exceptionalism: Explaining South Korean Responses. East Asian Science, Technology and Society: An International Journal 2022;16(1):7 View
  25. Zayed B, Talaia A, Gaaboobah M, Amer S, Mansour F. Google Trends as a predictive tool in the era of COVID-19: a scoping review. Postgraduate Medical Journal 2023;99(1175):962 View
  26. Yin J, Lui J. Factors influencing risk perception during Public Health Emergencies of International Concern (PHEIC): a scoping review. BMC Public Health 2024;24(1) View