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Google Trends Data and COVID‐19 in Europe: Correlations and model enhancement are European wide
Author(s) -
Sulyok Mihály,
Ferenci Tamás,
Walker Mark
Publication year - 2021
Publication title -
transboundary and emerging diseases
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.392
H-Index - 63
eISSN - 1865-1682
pISSN - 1865-1674
DOI - 10.1111/tbed.13887
Subject(s) - covid-19 , the internet , pandemic , lag , range (aeronautics) , geography , econometrics , computer science , data science , demography , disease , economics , world wide web , biology , engineering , medicine , sociology , infectious disease (medical specialty) , computer network , pathology , virology , outbreak , aerospace engineering
Summary 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.