Google Trends Data and COVID‐19 in Europe: Correlations and model enhancement are European wide
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 d...
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| Veröffentlicht in: | Transboundary and emerging diseases 2021-07, Vol.68 (4), p.2610-2615 |
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| container_title | Transboundary and emerging diseases |
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| creator | Sulyok, Mihály Ferenci, Tamás Walker, Mark |
| description | 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. |
| doi_str_mv | 10.1111/tbed.13887 |
| format | Article |
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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.</description><identifier>ISSN: 1865-1674</identifier><identifier>EISSN: 1865-1682</identifier><identifier>DOI: 10.1111/tbed.13887</identifier><language>eng</language><publisher>Berlin: Hindawi Limited</publisher><subject>Coronaviruses ; Correlation ; COVID-19 ; Data search ; Google Trends ; Internet ; Modelling ; Pandemics ; SARS‐CoV‐2 ; Search engines ; surveillance ; Trends ; Viral diseases</subject><ispartof>Transboundary and emerging diseases, 2021-07, Vol.68 (4), p.2610-2615</ispartof><rights>2020 The Authors. published by Wiley‐VCH GmbH</rights><rights>2020. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3707-7586e3ebb18bfc53c336b30cf2ec5859260f214fd9353aad45569f39935c85713</citedby><cites>FETCH-LOGICAL-c3707-7586e3ebb18bfc53c336b30cf2ec5859260f214fd9353aad45569f39935c85713</cites><orcidid>0000-0002-6960-5126</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Ftbed.13887$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Ftbed.13887$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,1416,27923,27924,45573,45574</link.rule.ids></links><search><creatorcontrib>Sulyok, Mihály</creatorcontrib><creatorcontrib>Ferenci, Tamás</creatorcontrib><creatorcontrib>Walker, Mark</creatorcontrib><title>Google Trends Data and COVID‐19 in Europe: Correlations and model enhancement are European wide</title><title>Transboundary and emerging diseases</title><description>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. 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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.</abstract><cop>Berlin</cop><pub>Hindawi Limited</pub><doi>10.1111/tbed.13887</doi><tpages>6</tpages><orcidid>https://orcid.org/0000-0002-6960-5126</orcidid><oa>free_for_read</oa></addata></record> |
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| source | Wiley Online Library All Journals |
| subjects | Coronaviruses Correlation COVID-19 Data search Google Trends Internet Modelling Pandemics SARS‐CoV‐2 Search engines surveillance Trends Viral diseases |
| title | Google Trends Data and COVID‐19 in Europe: Correlations and model enhancement are European wide |
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