Assessment of the Impact of Media Coverage on COVID-19–Related Google Trends Data: Infodemiology Study

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Abstract

Background

The influence of media coverage on web-based searches may hinder the role of Google Trends (GT) in monitoring coronavirus disease (COVID-19).

Objective

The aim of this study was to assess whether COVID-19–related GT data, particularly those related to ageusia and anosmia, were primarily related to media coverage or to epidemic trends.

Methods

We retrieved GT query data for searches on coronavirus, cough, anosmia, and ageusia and plotted them over a period of 5 years. In addition, we analyzed the trends of those queries for 17 countries throughout the year 2020 with a particular focus on the rises and peaks of the searches. For anosmia and ageusia, we assessed whether the respective GT data correlated with COVID-19 cases and deaths both throughout 2020 and specifically before March 16, 2020 (ie, the date when the media started reporting that these symptoms can be associated with COVID-19).

Results

Over the last five years, peaks for coronavirus searches in GT were only observed during the winter of 2020. Rises and peaks in coronavirus searches appeared at similar times in the 17 different assessed countries irrespective of their epidemic situations. In 15 of these countries, rises in anosmia and ageusia searches occurred in the same week or 1 week after they were identified in the media as symptoms of COVID-19. When data prior to March 16, 2020 were analyzed, anosmia and ageusia GT data were found to have variable correlations with COVID-19 cases and deaths in the different countries.

Conclusions

Our results indicate that COVID-19–related GT data are more closely related to media coverage than to epidemic trends.

Related collections

Most cited references 14

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Olfactory and gustatory dysfunctions as a clinical presentation of mild-to-moderate forms of the coronavirus disease (COVID-19): a multicenter European study

Jerome R. Lechien, Carlos M Chiesa-Estomba, Daniele De Siati … (2020)

Objective To investigate the occurrence of olfactory and gustatory dysfunctions in patients with laboratory-confirmed COVID-19 infection. Methods Patients with laboratory-confirmed COVID-19 infection were recruited from 12 European hospitals. The following epidemiological and clinical outcomes have been studied: age, sex, ethnicity, comorbidities, and general and otolaryngological symptoms. Patients completed olfactory and gustatory questionnaires based on the smell and taste component of the National Health and Nutrition Examination Survey, and the short version of the Questionnaire of Olfactory Disorders-Negative Statements (sQOD-NS). Results A total of 417 mild-to-moderate COVID-19 patients completed the study (263 females). The most prevalent general symptoms consisted of cough, myalgia, and loss of appetite. Face pain and nasal obstruction were the most disease-related otolaryngological symptoms. 85.6% and 88.0% of patients reported olfactory and gustatory dysfunctions, respectively. There was a significant association between both disorders (p < 0.001). Olfactory dysfunction (OD) appeared before the other symptoms in 11.8% of cases. The sQO-NS scores were significantly lower in patients with anosmia compared with normosmic or hyposmic individuals (p = 0.001). Among the 18.2% of patients without nasal obstruction or rhinorrhea, 79.7% were hyposmic or anosmic. The early olfactory recovery rate was 44.0%. Females were significantly more affected by olfactory and gustatory dysfunctions than males (p = 0.001). Conclusion Olfactory and gustatory disorders are prevalent symptoms in European COVID-19 patients, who may not have nasal symptoms. The sudden anosmia or ageusia need to be recognized by the international scientific community as important symptoms of the COVID-19 infection. Electronic supplementary material The online version of this article (10.1007/s00405-020-05965-1) contains supplementary material, which is available to authorized users.

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When Google got flu wrong.

Declan Butler (2013)

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Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study

Seyed Ayyoubzadeh, Seyed Mehdi Ayyoubzadeh, Hoda Zahedi … (2020)

Background The recent global outbreak of coronavirus disease (COVID-19) is affecting many countries worldwide. Iran is one of the top 10 most affected countries. Search engines provide useful data from populations, and these data might be useful to analyze epidemics. Utilizing data mining methods on electronic resources’ data might provide a better insight into the COVID-19 outbreak to manage the health crisis in each country and worldwide. Objective This study aimed to predict the incidence of COVID-19 in Iran. Methods Data were obtained from the Google Trends website. Linear regression and long short-term memory (LSTM) models were used to estimate the number of positive COVID-19 cases. All models were evaluated using 10-fold cross-validation, and root mean square error (RMSE) was used as the performance metric. Results The linear regression model predicted the incidence with an RMSE of 7.562 (SD 6.492). The most effective factors besides previous day incidence included the search frequency of handwashing, hand sanitizer, and antiseptic topics. The RMSE of the LSTM model was 27.187 (SD 20.705). Conclusions Data mining algorithms can be employed to predict trends of outbreaks. This prediction might support policymakers and health care managers to plan and allocate health care resources accordingly.

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Author and article information

Contributors

Bernardo Sousa-Pinto:

ORCID: https://orcid.org/0000-0002-1277-3401

Department of Community Medicine, Information and Health Decision SciencesFaculty of MedicineUniversity of PortoRua Plácido Costa s/nPorto, 4400Portugal351 912362153bernardosousapinto@protonmail.com

Journal

Journal ID (nlm-ta): J Med Internet Res

Journal ID (iso-abbrev): J. Med. Internet Res

Journal ID (publisher-id): JMIR

Title: Journal of Medical Internet Research

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Print): 1439-4456

ISSN (Electronic): 1438-8871

Publication date Collection: August 2020

Publication date (Electronic): 10 August 2020

Publication date PMC-release: 10 August 2020

Volume: 22

Issue: 8

Electronic Location Identifier: e19611

Affiliations

[1 ] Department of Community Medicine, Information and Health Decision Sciences Faculty of Medicine University of Porto Porto Portugal

[2 ] Center for Health Technology and Services Research University of Porto Porto Portugal

[3 ] MASK-air Montpellier France

[4 ] Medical Consulting Czarlewski Levallois France

[5 ] Centre for Research in Environmental Epidemiology Barcelona Institute for Global Health Barcelona Spain

[6 ] Universitat Pompeu Fabra Barcelona Spain

[7 ] CIBER Epidemiología y Salud Pública Barcelona Spain

[8 ] Charité, Universitätsmedizin Berlin Humboldt-Universität zu Berlin Berlin Germany

[9 ] Comprehensive Allergy Center Department of Dermatology and Allergy Berlin Institute of Health Berlin Germany

[10 ] MACVIA-France Montpellier France

Author notes

Corresponding Author: Bernardo Sousa-Pinto bernardosousapinto@ 123456protonmail.com

Author information

Bernardo Sousa-Pinto https://orcid.org/0000-0002-1277-3401

Aram Anto https://orcid.org/0000-0001-8180-5481

Wienia Czarlewski https://orcid.org/0000-0002-6180-3232

Josep M Anto https://orcid.org/0000-0002-4736-8529

João Almeida Fonseca https://orcid.org/0000-0002-0887-8796

Jean Bousquet https://orcid.org/0000-0002-4061-4766

Article

Publisher ID: v22i8e19611

DOI: 10.2196/19611

PMC ID: 7423386

PubMed ID: 32530816

SO-VID: 31a7a295-49b7-4d46-abf3-35c63f8340f4

Copyright © ©Bernardo Sousa-Pinto, Aram Anto, Wienia Czarlewski, Josep M Anto, João Almeida Fonseca, Jean Bousquet. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 10.08.2020.

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 24 April 2020

Date revision requested : 23 May 2020

Date revision received : 29 May 2020

Date accepted : 11 June 2020

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