%0 Journal Article
%@ 2563-6316
%I JMIR Publications
%V 3
%N 2
%P e35356
%T Google Trends as a Predictive Tool for COVID-19 Vaccinations in Italy: Retrospective Infodemiological Analysis
%A Rovetta,Alessandro
%+ R&C Research, Via Brede Traversa II, Bovezzo, 25073, Italy, 39 3927112808, rovetta.mresearch@gmail.com
%K COVID-19
%K epidemiology
%K Google Trends
%K infodemiology
%K infoveillance
%K Italy
%K public health
%K SARS-CoV-2
%K vaccinations
%K vaccines
%K social media analysis
%K social media
%D 2022
%7 19.4.2022
%9 Original Paper
%J JMIRx Med
%G English
%X Background: 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. Objective: This paper aims to provide an effective and efficient approach to investigating vaccine adherence against COVID-19 via Google Trends. Methods: 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 (z) and percentage difference (δ) were used to compare Spearman coefficients. A regression model V=f(VRH, VRQ) was built to validate the results found. The Holm-Bonferroni correction was adopted (P*). SEs are reported. Results: 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 r²=0.460, P*<.001, lag 0 weeks; max r²=0.903, P*<.001, lag 6 weeks). The remaining cross-correlations have been markedly lower (δ>55.8%; z>5.8; P*<.001). The regression model confirmed the greater significance of VRQ versus VRH (P*<.001 vs P=.03, P*=.29). Conclusions: 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. 
%M 35481982
%R 10.2196/35356
%U https://med.jmirx.org/2022/2/e35356
%U https://doi.org/10.2196/35356
%U http://www.ncbi.nlm.nih.gov/pubmed/35481982