Tracking COVID-19 in Europe: Infodemiology Approach.
COVID-19
Google Trends
big data
coronavirus
infodemiology
infoveillance
Journal
JMIR public health and surveillance
ISSN: 2369-2960
Titre abrégé: JMIR Public Health Surveill
Pays: Canada
ID NLM: 101669345
Informations de publication
Date de publication:
20 04 2020
20 04 2020
Historique:
received:
28
03
2020
accepted:
02
04
2020
pubmed:
7
4
2020
medline:
25
4
2020
entrez:
7
4
2020
Statut:
epublish
Résumé
Infodemiology (ie, information epidemiology) uses web-based data to inform public health and policy. Infodemiology metrics have been widely and successfully used to assess and forecast epidemics and outbreaks. In light of the recent coronavirus disease (COVID-19) pandemic that started in Wuhan, China in 2019, online search traffic data from Google are used to track the spread of the new coronavirus disease in Europe. Time series from Google Trends from January to March 2020 on the Topic (Virus) of "Coronavirus" were retrieved and correlated with official data on COVID-19 cases and deaths worldwide and in the European countries that have been affected the most: Italy (at national and regional level), Spain, France, Germany, and the United Kingdom. Statistically significant correlations are observed between online interest and COVID-19 cases and deaths. Furthermore, a critical point, after which the Pearson correlation coefficient starts declining (even if it is still statistically significant) was identified, indicating that this method is most efficient in regions or countries that have not yet peaked in COVID-19 cases. In the past, infodemiology metrics in general and data from Google Trends in particular have been shown to be useful in tracking and forecasting outbreaks, epidemics, and pandemics as, for example, in the cases of the Middle East respiratory syndrome, Ebola, measles, and Zika. With the COVID-19 pandemic still in the beginning stages, it is essential to explore and combine new methods of disease surveillance to assist with the preparedness of health care systems at the regional level.
Sections du résumé
BACKGROUND
Infodemiology (ie, information epidemiology) uses web-based data to inform public health and policy. Infodemiology metrics have been widely and successfully used to assess and forecast epidemics and outbreaks.
OBJECTIVE
In light of the recent coronavirus disease (COVID-19) pandemic that started in Wuhan, China in 2019, online search traffic data from Google are used to track the spread of the new coronavirus disease in Europe.
METHODS
Time series from Google Trends from January to March 2020 on the Topic (Virus) of "Coronavirus" were retrieved and correlated with official data on COVID-19 cases and deaths worldwide and in the European countries that have been affected the most: Italy (at national and regional level), Spain, France, Germany, and the United Kingdom.
RESULTS
Statistically significant correlations are observed between online interest and COVID-19 cases and deaths. Furthermore, a critical point, after which the Pearson correlation coefficient starts declining (even if it is still statistically significant) was identified, indicating that this method is most efficient in regions or countries that have not yet peaked in COVID-19 cases.
CONCLUSIONS
In the past, infodemiology metrics in general and data from Google Trends in particular have been shown to be useful in tracking and forecasting outbreaks, epidemics, and pandemics as, for example, in the cases of the Middle East respiratory syndrome, Ebola, measles, and Zika. With the COVID-19 pandemic still in the beginning stages, it is essential to explore and combine new methods of disease surveillance to assist with the preparedness of health care systems at the regional level.
Identifiants
pubmed: 32250957
pii: v6i2e18941
doi: 10.2196/18941
pmc: PMC7173241
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e18941Informations de copyright
©Amaryllis Mavragani. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 20.04.2020.
Références
J Med Internet Res. 2009 Mar 27;11(1):e11
pubmed: 19329408
J Med Internet Res. 2017 Jun 13;19(6):e193
pubmed: 28611015
JMIR Public Health Surveill. 2019 May 29;5(2):e13439
pubmed: 31144671
PLoS Curr. 2009 Sep 03;1:RRN1036
pubmed: 20025200
Health Info Libr J. 2020 Apr 6;:
pubmed: 32251543
JMIR Public Health Surveill. 2018 Feb 09;4(1):e16
pubmed: 29426815
J Med Internet Res. 2003 Apr-Jun;5(2):e14
pubmed: 12857670
JMIR Public Health Surveill. 2018 Nov 22;4(4):e10827
pubmed: 30467106
J Med Internet Res. 2018 Jul 09;20(7):e236
pubmed: 29986843
BMC Infect Dis. 2016 Aug 25;16(1):448
pubmed: 27562369
AMIA Annu Symp Proc. 2006;:244-8
pubmed: 17238340
Epidemiol Infect. 2016 Jul;144(10):2136-43
pubmed: 26939535
J Med Internet Res. 2018 Nov 06;20(11):e270
pubmed: 30401664
Am J Prev Med. 2011 May;40(5 Suppl 2):S154-8
pubmed: 21521589