Rovetta Alessandro
R&C Research Bovezzo Italy.
JMIRx Med. 2022 Apr 19;3(2):e35356. doi: 10.2196/35356. eCollection 2022 Apr-Jun.
Google Trends is an infoveillance tool widely used by the scientific community to investigate different user behaviors related to COVID-19. However, several limitations regarding its adoption are reported in the literature.
This paper aims to provide an effective and efficient approach to investigating vaccine adherence against COVID-19 via Google Trends.
Through the cross-correlational analysis of well-targeted hypotheses, we investigate the predictive capacity of web searches related to COVID-19 toward vaccinations in Italy from November 2020 to November 2021. The keyword "vaccine reservation" query (VRQ) was chosen as it reflects a real intention of being vaccinated (V). Furthermore, the impact of the second most read Italian newspaper (vaccine-related headlines [VRH]) on vaccine-related web searches was investigated to evaluate the role of the mass media as a confounding factor. Fisher r-to-z transformation () and percentage difference (δ) were used to compare Spearman coefficients. A regression model was built to validate the results found. The Holm-Bonferroni correction was adopted (*). SEs are reported.
Simple and generic keywords are more likely to identify the actual web interest in COVID-19 vaccines than specific and elaborated keywords. Cross-correlations between VRQ and V were very strong and significant (min ²=0.460, *<.001, lag 0 weeks; max ²=0.903, *<.001, lag 6 weeks). The remaining cross-correlations have been markedly lower (δ>55.8%; >5.8; <.001). The regression model confirmed the greater significance of VRQ versus VRH (<.001 vs =.03, *=.29).
This research provides preliminary evidence in favor of using Google Trends as a surveillance and prediction tool for vaccine adherence against COVID-19 in Italy. Further research is needed to establish the appropriate use and limits of Google Trends for vaccination tracking. However, these findings prove that the search for suitable keywords is a fundamental step to reduce confounding factors. Additionally, targeting hypotheses helps diminish the likelihood of spurious correlations. It is recommended that Google Trends be leveraged as a complementary infoveillance tool by government agencies to monitor and predict vaccine adherence in this and future crises by following the methods proposed in this paper.
谷歌趋势是一种信息监测工具，被科学界广泛用于调查与新冠疫情相关的不同用户行为。然而，文献中报道了其应用存在的一些局限性。
本文旨在提供一种有效且高效的方法，通过谷歌趋势来调查针对新冠疫情的疫苗接种依从性。
通过对精心设定的假设进行交叉相关分析，我们调查了2020年11月至2021年11月期间意大利与新冠疫情相关的网络搜索对疫苗接种的预测能力。选择关键词“疫苗预约”查询（VRQ），因为它反映了实际的接种意愿（V）。此外，还研究了意大利第二大阅读量报纸（与疫苗相关的头条新闻[VRH]）对与疫苗相关的网络搜索的影响，以评估大众媒体作为混杂因素的作用。使用费舍尔r到z变换（）和百分比差异（δ）来比较斯皮尔曼系数。建立回归模型以验证所发现的结果。采用霍尔姆 - 邦费罗尼校正（*）。报告了标准误。
简单通用的关键词比具体详细的关键词更有可能识别出对新冠疫苗的实际网络关注度。VRQ与V之间的交叉相关性非常强且具有统计学意义（最小值² = 0.460，* <.001，滞后0周；最大值² = 0.903，* <.001，滞后6周）。其余的交叉相关性则明显较低（δ > 55.8%；> 5.8；* <.001）。回归模型证实了VRQ相对于VRH具有更大的显著性（* <.001对 = 0.03，* = 0.29）。
本研究提供了初步证据，支持将谷歌趋势用作意大利针对新冠疫情的疫苗接种依从性的监测和预测工具。需要进一步研究以确定谷歌趋势在疫苗接种跟踪方面的适当用途和局限性。然而，这些发现证明，寻找合适的关键词是减少混杂因素的关键步骤。此外，设定假设有助于降低虚假相关性的可能性。建议政府机构利用谷歌趋势作为一种补充性的信息监测工具，通过遵循本文提出的方法来监测和预测本次及未来危机中的疫苗接种依从性。
