» Articles » PMID: 32183935

Retrospective Analysis of the Possibility of Predicting the COVID-19 Outbreak from Internet Searches and Social Media Data, China, 2020

Overview
Journal Euro Surveill
Date 2020 Mar 19
PMID 32183935
Citations 147
Authors
Affiliations
Soon will be listed here.
Abstract

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.

Citing Articles

Google Trends applications for COVID-19 pandemic: A bibliometric analysis.

Li H, Zhang N, Ma X, Wang Y, Yang F, Wang W Digit Health. 2025; 11:20552076241310055.

PMID: 39758260 PMC: 11696959. DOI: 10.1177/20552076241310055.


Early warning and predicting of COVID-19 using zero-inflated negative binomial regression model and negative binomial regression model.

Zhou W, Huang D, Liang Q, Huang T, Wang X, Pei H BMC Infect Dis. 2024; 24(1):1006.

PMID: 39300391 PMC: 11414173. DOI: 10.1186/s12879-024-09940-7.


The predation relationship between online medical search and online medical consultation-empirical research based on Baidu platform data.

Wang Y, Ran L, Jiao W, Xia Y, Lan Y Front Public Health. 2024; 12:1392743.

PMID: 39267654 PMC: 11390467. DOI: 10.3389/fpubh.2024.1392743.


Incorporating connectivity among Internet search data for enhanced influenza-like illness tracking.

Ning S, Hussain A, Wang Q PLoS One. 2024; 19(8):e0305579.

PMID: 39186560 PMC: 11346739. DOI: 10.1371/journal.pone.0305579.


Short- and Long-Term Predicted and Witnessed Consequences of Digital Surveillance During the COVID-19 Pandemic: Scoping Review.

Comer L, Donelle L, Hiebert B, Smith M, Kothari A, Stranges S JMIR Public Health Surveill. 2024; 10:e47154.

PMID: 38788212 PMC: 11129783. DOI: 10.2196/47154.


References
1.
Majumder M, Santillana M, Mekaru S, McGinnis D, Khan K, Brownstein J . 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. PMC: 4909981. DOI: 10.2196/publichealth.5814. View

2.
Shin S, Seo D, An J, Kwak H, Kim S, Gwack J . High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea. Sci Rep. 2016; 6:32920. PMC: 5011762. DOI: 10.1038/srep32920. View

3.
Santangelo O, 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):385-391. DOI: 10.7416/ai.2019.2300. View

4.
Wilson N, Mason K, Tobias M, Peacey M, Huang Q, Baker M . Interpreting Google flu trends data for pandemic H1N1 influenza: the New Zealand experience. Euro Surveill. 2009; 14(44). View

5.
Wu J, Leung K, Leung G . 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):689-697. PMC: 7159271. DOI: 10.1016/S0140-6736(20)30260-9. View