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

被引:15
作者
Sulyok, Mihaly [1 ,2 ]
Ferenci, Tamas [3 ,4 ]
Walker, Mark [5 ]
机构
[1] Eberhard Karls Univ Tubingen, Inst Trop Med, Wilhelmstr 27, D-72074 Tubingen, Germany
[2] Eberhard Karls Univ Tubingen, Dept Pathol & Neuropathol, Tubingen, Germany
[3] Obuda Univ, Physiol Controls Res Ctr, Budapest, Hungary
[4] Corvinus Univ Budapest, Dept Stat, Budapest, Hungary
[5] Sheffield Hallam Univ, Dept Nat & Built Environm, Sheffield, S Yorkshire, England
关键词
COVID-19; Google Trends; model; SARS-CoV-2; surveillance; SEARCH; COMMUNICATION; INFODEMIOLOGY; INTERNET;
D O I
10.1111/tbed.13887
中图分类号
R51 [传染病];
学科分类号
100401 ;
摘要
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.
引用
收藏
页码:2610 / 2615
页数:6
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