文章基本信息

标题：Time-Series Associations between Public Interest in COVID-19 Variants and National Vaccination Rate: A Google Trends Analysis
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作者：Cecilia Cheng ; Davide Mazzoni ; Ilaria Cutica 等
期刊名称：Behavioral Sciences
电子版ISSN：2076-328X
出版年度：2022
卷号：12
期号：7
DOI：10.3390/bs12070223
语种：English
出版社：MDPI Publishing
摘要：The emergence of a constantly mutating novel virus has led to considerable public anxiety amid the COVID-19 pandemic. Information seeking is a common strategy to cope with pandemic anxiety. Using Google Trends analysis, this study investigated public interest in COVID-19 variants and its temporal associations with the disease-prevention measure of vaccination during the initial COVID-19 vaccine rollout period (13 December 2020 to 25 September 2021). Public interest was operationalized as the relative search volume of online queries of variant-related terms in the countries first affected by the Alpha, Beta, and Delta variants: the UK, South Africa, and India, respectively. The results show that public interest in COVID-19 variants was greater during the Delta-variant-predominant period than before this period. The time-series cross-correlation analysis revealed positive temporal associations (i.e., greater such public interest was accompanied by an increase in national vaccination rate) tended to occur more frequently and at earlier time lags than the negative temporal associations. This study yielded new findings regarding the temporal changes in public interest in COVID-19 variants, and the between-country variations in these public interest changes can be explained by differences in the rate and pace of vaccination among the countries of interest.
关键词：eninformation seekingsearch queryinfodemiologyinfosurveillancecopingpandemic anxiety