Literature DB >> 33085851

Google Trends Data and COVID-19 in Europe: Correlations and model enhancement are European wide.

Mihály Sulyok1,2, Tamás Ferenci3,4, Mark Walker5.   

Abstract

The current COVID-19 pandemic offers a unique opportunity to examine the utility of Internet search data in disease modelling across multiple countries. Most such studies typically examine trends within only a single country, with few going beyond describing the relationship between search data patterns and disease occurrence. Google Trends data (GTD) indicating the volume of Internet searching on 'coronavirus' were obtained for a range of European countries along with corresponding incident case numbers. Significant positive correlations between GTD with incident case numbers occurred across European countries, with the strongest correlations being obtained using contemporaneous data for most countries. GTD was then integrated into a distributed lag model; this improved model quality for both the increasing and decreasing epidemic phases. These results show the utility of Internet search data in disease modelling, with possible implications for cross country analysis.
© 2020 The Authors. Transboundary and Emerging Diseases published by Wiley-VCH GmbH.

Entities:  

Keywords:  COVID-19; Google Trends; SARS-CoV-2; model; surveillance

Year:  2020        PMID: 33085851     DOI: 10.1111/tbed.13887

Source DB:  PubMed          Journal:  Transbound Emerg Dis        ISSN: 1865-1674            Impact factor:   5.005


  6 in total

1.  COVID-19 and thyroid disease: An infodemiological pilot study.

Authors:  Ioannis Ilias; Charalampos Milionis; Eftychia Koukkou
Journal:  World J Methodol       Date:  2022-05-20

2.  Epidemics, Public Sentiment, and Infectious Disease Equity Market Volatility.

Authors:  Jinxia Meng; Qingyi Su; Jinhua Zhang; Li Wang; Ruihui Xu; Cheng Yan
Journal:  Front Public Health       Date:  2021-05-14

3.  Google Trends as a Predictive Tool for COVID-19 Vaccinations in Italy: Retrospective Infodemiological Analysis.

Authors:  Alessandro Rovetta
Journal:  JMIRx Med       Date:  2022-04-19

Review 4.  Forecasting and Surveillance of COVID-19 Spread Using Google Trends: Literature Review.

Authors:  Tobias Saegner; Donatas Austys
Journal:  Int J Environ Res Public Health       Date:  2022-09-29       Impact factor: 4.614

5.  Using Google Trends to Predict COVID-19 Vaccinations and Monitor Search Behaviours about Vaccines: A Retrospective Analysis of Italian Data.

Authors:  Andrea Maugeri; Martina Barchitta; Antonella Agodi
Journal:  Vaccines (Basel)       Date:  2022-01-14

6.  How COVID-19 Has Influenced Public Interest in Antimicrobials, Antimicrobial Resistance and Related Preventive Measures: A Google Trends Analysis of Italian Data.

Authors:  Andrea Maugeri; Martina Barchitta; Guido Basile; Antonella Agodi
Journal:  Antibiotics (Basel)       Date:  2022-03-13
  6 in total

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