Google Trends Data and COVID‐19 in Europe: Correlations and model enhancement are European wide
| Autor: | Mihály Sulyok, Tamás Ferenci, Mark D. Walker |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Distributed lag
Disease occurrence Coronavirus disease 2019 (COVID-19) 040301 veterinary sciences 0403 veterinary science 03 medical and health sciences Econometrics Range (statistics) Animals Data patterns Pandemics 030304 developmental biology Cross country analysis Internet 0303 health sciences General Veterinary General Immunology and Microbiology SARS-CoV-2 business.industry COVID-19 04 agricultural and veterinary sciences General Medicine Europe Search Engine Disease modelling Geography The Internet business |
| Zdroj: | Transboundary and Emerging Diseases |
| ISSN: | 1865-1682 1865-1674 |
| DOI: | 10.1111/tbed.13887 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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