Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020
Open accessAbstract
The peak of Internet searches and social media data about the coronavirus disease 2019 (COVID-19) outbreak occurred 10–14 days earlier than the peak of daily incidences in China. Internet searches and social media data had high correlation with daily incidences, with the maximum r > 0.89 in all correlations. The lag correlations also showed a maximum correlation at 8–12 days for laboratory-confirmed cases and 6–8 days for suspected cases.
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Dramatic increment of searching the topics about 'Coronavirus' or 'Pneumonia' on Google, Baidu Index, and Sina Weibo Index were observed in China at the beginning of the COVID-19 pandemic.
“Dramatic increment of searching the topics about ‘Coronavirus’ or ‘Pneumonia’ on Google, Baidu Index, and Sina Weibo Index were observed in China at the beginning of the COVID-19 pandemic [44].”
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Cites
Higgins, T. S., Wu, A. W., Sharma, D., Illing, E. A., Rubel, K., Ting, J. Y., & Snot Force Alliance (2020) · JMIR Public Health and Surveillance · doi:10.2196/19702 · Cited by 129 · see citing works
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- [1]Wu JT Leung K Leung GM Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet. 2020; 395(10225): 689- 97. 10.1016/S0140-6736(20)30260-9 32014114 PMC7159271 · DOI · Paper page
- [2]Novel Coronavirus Pneumonia Emergency Response Epidemiology Team [The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China]. Zhonghua Liu Xing Bing Xue Za Zhi. 2020; 41(2): 145- 51. Chinese. 32064853
- [4]Ginsberg J Mohebbi MH Patel RS Brammer L Smolinski MS Brilliant L Detecting influenza epidemics using search engine query data. Nature. 2009; 457(7232): 1012- 4. 10.1038/nature07634 19020500 · DOI · Paper page
- [5]Marques-Toledo CA Degener CM Vinhal L Coelho G Meira W Codeço CT Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level. PLoS Negl Trop Dis. 2017; 11(7): e0005729. 10.1371/journal.pntd.0005729 28719659 PMC5533462 · DOI
- [6]Wilson N Mason K Tobias M Peacey M Huang QS Baker M Interpreting Google flu trends data for pandemic H1N1 influenza: the New Zealand experience. Euro Surveill. 2009; 14(44): 19386. 19941777
- [7]Majumder MS Santillana M Mekaru SR McGinnis DP Khan K Brownstein JS Utilizing nontraditional data sources for near real-time estimation of transmission dynamics during the 2015-2016 Colombian Zika virus disease outbreak. JMIR Public Health Surveill. 2016; 2(1): e30. 10.2196/publichealth.5814 27251981 PMC4909981 · DOI
- [8]Santangelo OE Provenzano S Piazza D Giordano D Calamusa G Firenze A Digital epidemiology: assessment of measles infection through Google Trends mechanism in Italy. Ann Ig. 2019; 31(4): 385- 91. 31268123
- [9]Shin SY Seo DW An J Kwak H Kim SH Gwack J High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea. Sci Rep. 2016; 6(1): 32920. 10.1038/srep32920 27595921 PMC5011762 · DOI
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What social media told us in the time of COVID-19: A scoping review
Tsao, S.-F., Chen, H., Tisseverasinghe, T., Yang, Y., Li, L., & Butt, Z. A. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30315-0 · Cited by 816 · see citing works
Self-citationFagherazzi, G., Goetzinger, C., Rashid, M. A., Aguayo, G. A., & Huiart, L. (2020) · Journal of Medical Internet Research · doi:10.2196/19284 · Cited by 426 · see citing works
Mackey, T., Purushothaman, V., Li, J., Shah, N., Nali, M., Bardier, C., Liang, B., Cai, M., & Cuomo, R. (2020) · JMIR Public Health and Surveillance · doi:10.2196/19509 · Cited by 142 · see citing works
COVID-19 predictability in the United States using Google Trends time series
Mavragani, A., & Gkillas, K. (2020) · Scientific Reports · doi:10.1038/s41598-020-77275-9 · Cited by 140 · see citing works
Higgins, T. S., Wu, A. W., Sharma, D., Illing, E. A., Rubel, K., Ting, J. Y., & Snot Force Alliance (2020) · JMIR Public Health and Surveillance · doi:10.2196/19702 · Cited by 129 · see citing works
Htay, M. N. N., Parial, L. L., Tolabing, M. C., Dadaczynski, K., Okan, O., Leung, A. Y. M., & Su, T. T. (2022) · PLOS ONE · doi:10.1371/journal.pone.0266276 · Cited by 78 · see citing works
Kurian, S. J., Bhatti, A. U. R., Alvi, M. A., Ting, H. H., Storlie, C., Wilson, P. M., Shah, N. D., Liu, H., & Bydon, M. (2020) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2020.08.022 · Cited by 69 · see citing works
COVID-19 Symptom-Related Google Searches and Local COVID-19 Incidence in Spain: Correlational Study
Jimenez, A. J., Estevez-Reboredo, R. M., Santed, M. A., & Ramos, V. (2020) · Journal of Medical Internet Research · doi:10.2196/23518 · Cited by 47 · see citing works
Glasdam, S., & Stjernswärd, S. (2020) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2020.100090 · Cited by 31 · see citing works
Fischer, A. E., Van Tonder, T., Gumede, S. B., & Lalla-Edward, S. T. (2021) · JMIR Formative Research · doi:10.2196/25273 · Cited by 11 · see citing works
- Authors
- Li, C., Chen, L. J., Chen, X., Zhang, M., Pang, C. P., & Chen, H.
- Affiliations
- Chinese University of Hong Kong (HK)
- Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou, China
- Venue
- Eurosurveillance
- Published
- 2020-03-12
- Volume
- 25
- Issue
- 10
- ISSN
- 1025-496X (ISSN-L)
- 1560-7917
- PMC
- PMC7078825
- OpenAlex
- W3011486546
- Cited by
- 313
- References
- 1
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 16 · 2023: 31 · 2022: 62 · 2021: 107 · 2020: 86
- FWCI
- 50.09
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Eurosurveillance (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by)
- Topics
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- COVID-19 pandemic, epidemic forecasting, internet search data, social media data, lag correlation, laboratory-confirmed cases, suspected cases
- MeSH terms
- COVID-19, China, Disease Outbreaks, Humans, Laboratories, Pneumonia, Viral, Population Surveillance, Public Health Practice, Incidence, Coronavirus Infections, Internet, Search Engine, Social Media, Web Browser
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