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Cited by 35Data-Driven Disease SurveillanceCOVID-19 epidemiological studiesCommunication and COVID-19 Impact

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

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Husnayain, Atina, Shim, Eunha, Fuad, Anis, Su, Emily Chia-Yu·2020·Journal of Medical Internet Research
DOI 10.2196/19788PMC PMC7527166

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Abstract

Background: South Korea is among the best-performing countries in tackling the coronavirus pandemic by using mass drive-through testing, face mask use, and extensive social distancing. However, understanding the patterns of risk perception could also facilitate effective risk communication to minimize the impacts of disease spread during this crisis. Objective: We attempt to explore patterns of community health risk perceptions of COVID-19 in South Korea using internet search data. Methods: Google Trends (GT) and NAVER relative search volumes (RSVs) data were collected using COVID-19–related terms in the Korean language and were retrieved according to time, gender, age groups, types of device, and location. Online queries were compared to the number of daily new COVID-19 cases and tests reported in the Kaggle open-access data set for the time period of December 5, 2019, to May 31, 2020. Time-lag correlations calculated by Spearman rank correlation coefficients were employed to assess whether correlations between new COVID-19 cases and internet searches were affected by time. We also constructed a prediction model of new COVID-19 cases using the number of COVID-19 cases, tests, and GT and NAVER RSVs in lag periods (of 1-3 days). Single and multiple regressions were employed using backward elimination and a variance inflation factor of <5. Results: The numbers of COVID-19–related queries in South Korea increased during local events including local transmission, approval of coronavirus test kits, implementation of coronavirus drive-through tests, a face mask shortage, and a widespread campaign for social distancing as well as during international events such as the announcement of a Public Health Emergency of International Concern by the World Health Organization. Online queries were also stronger in women (r=0.763-0.823; P<.001) and age groups ≤29 years (r=0.726-0.821; P<.001), 30-44 years (r=0.701-0.826; P<.001), and ≥50 years (r=0.706-0.725; P<.001). In terms of spatial distribution, internet search data were higher in affected areas. Moreover, greater correlations were found in mobile searches (r=0.704-0.804; P<.001) compared to those of desktop searches (r=0.705-0.717; P<.001), indicating changing behaviors in searching for online health information during the outbreak. These varied internet searches related to COVID-19 represented community health risk perceptions. In addition, as a country with a high number of coronavirus tests, results showed that adults perceived coronavirus test–related information as being more important than disease-related knowledge. Meanwhile, younger, and older age groups had different perceptions. Moreover, NAVER RSVs can potentially be used for health risk perception assessments and disease predictions. Adding COVID-19–related searches provided by NAVER could increase the performance of the model compared to that of the COVID-19 case–based model and potentially be used to predict epidemic curves. Conclusions: The use of both GT and NAVER RSVs to explore patterns of community health risk perceptions could be beneficial for targeting risk communication from several perspectives, including time, population characteristics, and location.

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  • Growing interest in the current COVID-19 pandemic and online information search about COVID-19 were reported in many countries in Asia, Europe, and the United States.

    “Similarly, the growing interest in the current COVID-19 pandemic and online information search about COVID-19 were reported in many countries in Asia, Europe, and the United States [16, 45–48].”

    From Digital health literacy, online information-seeking behaviour, and satisfaction of Covid-19 information among the university students of East and South-East Asia (2022) · cites this paper as [47]

    The Results section reports surges in COVID-19 information searches in South Korea, supporting the claim for one Asian country. This paper does not establish that growing searches were reported in many countries across Asia, Europe, and the United States. (LLM, conf=0.86)

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Research works printed but not linked: 28

  1. [4]Shim E Tariq A Choi W Lee Y Chowell G Transmission potential and severity of COVID-19 in South Korea Int J Infect Dis 2020 4 93 339 344 10.1016/j.ijid.2020.03.031 32198088 32198088 PMC7118661 · DOI
  2. [6]Kwon KT Ko JH Shin H Sung M Kim JY Drive-through screening center for COVID-19: a safe and efficient screening system against massive community outbreak J Korean Med Sci 2020 3 23 35 11 e123 10.3346/jkms.2020.35.e123 32193904 32193904 PMC7086085 · DOI
  3. [7]Jang WM Kim U Jang DH Jung H Cho S Eun SJ Lee JY Influence of trust on two different risk perceptions as an affective and cognitive dimension during Middle East respiratory syndrome coronavirus (MERS-CoV) outbreak in South Korea: serial cross-sectional surveys BMJ Open 2020 3 04 10 3 e033026 10.1136/bmjopen-2019-033026 32139484 PMC7059523 32139484 · DOI
  4. [9]Hubner AY Hovick SR Understanding risk information seeking and processing during an infectious disease outbreak: the case of Zika virus Risk Anal 2020 6 40 6 1212 1225 10.1111/risa.13456 32067244 32067244 · DOI
  5. [10]Lohiniva A Sane J Sibenberg K Puumalainen T Salminen M Understanding coronavirus disease (COVID-19) risk perceptions among the public to enhance risk communication efforts: a practical approach for outbreaks, Finland, February 2020 Euro Surveill 2020 4 25 13 2000317 10.2807/1560-7917.ES.2020.25.13.2000317 32265008 PMC7140598 32265008 · DOI
  6. [11]Yang JZ Whose risk? Why did the U.S. public ignore information about the Ebola outbreak? Risk Anal 2019 8 39 8 1708 1722 10.1111/risa.13282 30726559 30726559 · DOI
  7. [12]Eysenbach G Infodemiology: the epidemiology of (mis)information Am J Med 2002 12 15 113 9 763 5 10.1016/s0002-9343(02)01473-0 12517369 12517369 · DOI
  8. [13]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 3 27 11 1 e11 10.2196/jmir.1157 19329408 19329408 PMC2762766 · DOI · Paper page
  9. [14]Mavragani A Infodemiology and infoveillance: scoping review J Med Internet Res 2020 4 28 22 4 e16206 10.2196/16206 32310818 32310818 PMC7189791 · DOI
  10. [15]Eysenbach G Infodemiology and infoveillance tracking online health information and cyberbehavior for public health Am J Prev Med 2011 5 40 5 Suppl 2 S154 8 10.1016/j.amepre.2011.02.006 21521589 21521589 · DOI
  11. [16]Shin S Kim T Seo D Sohn CH Kim S Ryoo SM Lee Y Lee JH Kim WY Lim KS Correlation between national influenza surveillance data and search queries from mobile devices and desktops in South Korea PLoS One 2016 11 7 e0158539 10.1371/journal.pone.0158539 27391028 27391028 PMC4938422 · DOI
  12. [17]Zhang Y Yakob L Bonsall MB Hu W Predicting seasonal influenza epidemics using cross-hemisphere influenza surveillance data and local internet query data Sci Rep 2019 3 01 9 1 3262 10.1038/s41598-019-39871-2 30824756 30824756 PMC6397245 · DOI
  13. [21]Strzelecki A Rizun M Infodemiological study using Google Trends on coronavirus epidemic in Wuhan, China Int J Onl Eng 2020 4 08 16 04 139 10.3991/ijoe.v16i04.13531 · DOI
  14. [22]Mavragani A Ochoa G Google Trends in infodemiology and infoveillance: methodology framework JMIR Public Health Surveill 2019 5 29 5 2 e13439 10.2196/13439 31144671 31144671 PMC6660120 · DOI · Paper page
  15. [24]Vatcheva KP Lee M McCormick JB Rahbar MH Multicollinearity in regression analyses conducted in epidemiologic studies Epidemiology (Sunnyvale) 2016 4 6 2 227 10.4172/2161-1165.1000227 27274911 27274911 PMC4888898 · DOI · Paper page
  16. [25]Chakrabarti A Ghosh JK Bandyopadhyay PS Forster MR AIC, BIC and recent advances in model selection Philosophy of Statistics 2011 Amsterdam Elsevier 583 605
  17. [26]Maresca T With COVID-19 cases in decline, South Korea opens up to a new normal United Press International 2020 5 06 2020-06-15 https://www.upi.com/Top_News/World-News/2020/05/06/With-COVID-19-cases-in-decline-South-Korea-opens-up-to-a-new-normal/4181588747240/
  18. [27]Normile D Coronavirus cases have dropped sharply in South Korea. What’s the secret to its success? Science 2020 3 17 2020-03-30 American Association for the Advancement of Science https://www.sciencemag.org/news/2020/03/coronavirus-cases-have-dropped-sharply-south-korea-whats-secret-its-success#
  19. [31]Press release: the updates on COVID-19 in Korea as of 22 March Korean Center for Disease Control and Prevention 2020 3 22 https://www.cdc.go.kr/board/board.es?mid=a30402000000&bid=0030
  20. [32]Shim E Mizumoto K Choi W Chowell G Estimating the risk of COVID-19 death during the course of the outbreak in Korea, February-May 2020 J Clin Med 2020 5 29 9 6 1641 10.3390/jcm9061641 32485871 PMC7356403 32485871 · DOI
  21. [33]Paek HJ Hove T Risk perceptions and risk characteristics Oxford Research Encyclopedia of Communication 2017 Oxford Oxford University Press
  22. [35]Liang B Scammon DL Incidence of online health information search: a useful proxy for public health risk perception J Med Internet Res 2013 6 17 15 6 e114 10.2196/jmir.2401 23773974 23773974 PMC3713924 · DOI
  23. [36]Li C Chen LJ Chen X Zhang M Pang CP Chen H Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020 Euro Surveill 2020 3 25 10 2000199 10.2807/1560-7917.ES.2020.25.10.2000199 32183935 PMC7078825 32183935 · DOI · Paper page
  24. [37]Kim H South Korea learned its successful COVID-19 strategy from a previous coronavirus outbreak: MERS Bulletin of the Atomic Scientists 2020 3 20 2020-04-19 https://thebulletin.org/2020/03/south-korea-learned-its-successful-covid-19-strategy-from-a-previous-coronavirus-outbreak-mers/
  25. [38]Keller M Blench M Tolentino H Freifeld CC Mandl KD Mawudeku A Eysenbach G Brownstein JS Use of unstructured event-based reports for global infectious disease surveillance Emerg Infect Dis 2009 5 15 5 689 95 10.3201/eid1505.081114 19402953 19402953 PMC2687026 · DOI
  26. [40]Zhou F Yu T Du R Fan G Liu Y Liu Z Xiang J Wang Y Song B Gu X Guan L Wei Y Li H Wu X Xu J Tu S Zhang Y Chen H Cao B Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study Lancet 2020 3 28 395 10229 1054 1062 10.1016/S0140-6736(20)30566-3 32171076 32171076 PMC7270627 · DOI · Paper page
  27. [41]Husnayain A Fuad A Su EC 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 6 95 221 223 10.1016/j.ijid.2020.03.021 32173572 32173572 PMC7270523 · DOI · Paper page
  28. [42]Sher L COVID-19, anxiety, sleep disturbances and suicide Sleep Med 2020 6 70 124 10.1016/j.sleep.2020.04.019 32408252 32408252 PMC7195057 · DOI

Cited by

Sorted by citations

Authors
Husnayain, A., Shim, E., Fuad, A., & Su, E. C.-Y.
Affiliations
  • Universitas Gadjah Mada (ID)
  • Soongsil University (KR)
  • Taipei Medical University Hospital (TW)
Corresponding author
Su, Emily Chia-Yu
ORCID
Venue
Journal of Medical Internet Research
Publisher
JMIR Publications Inc.
Published
2020-09-29
Volume
22
Issue
9
Pages
e19788
ISSN
1438-8871 (ISSN-L)
DOI
10.2196/19788
PMC
PMC7527166
OpenAlex
W3085289084
Cited by
35
References
0
Citations by year
2025: 1 · 2024: 3 · 2023: 3 · 2022: 8 · 2021: 18 · 2020: 2
FWCI
5.55
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Medical Internet Research (publishedVersion, cc-by)
  • medRxiv (acceptedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Research Foundation
  • National Research Foundation of Korea (2018R1C1B6001723)
  • Ministry of Education, India
  • Ministry of Science and Technology, Taiwan (MOST108-2221-E038-018, MOST109-2221-E038-018)
  • Ministry of Science and ICT, South Korea
Topics
  • Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
  • Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
  • Social Sciences › Communication › Communication and COVID-19 Impact
Keywords
COVID-19 pandemic, risk perception, community health, internet search data, Google Trends, relative search volume, Naver, risk communication, COVID-19 case prediction, lag correlation, gender differences, age differences, South Korea, pandemic response
MeSH terms
COVID-19, COVID-19 Testing, Adolescent, Adult, Communication, Disease Outbreaks, Female, Humans, Male, Middle Aged, Pneumonia, Viral, Public Health, Public Opinion, Time Factors, Coronavirus Infections, Risk Assessment, Clinical Laboratory Techniques, Internet, Young Adult, Republic of Korea, Search Engine, Pandemics

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