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      Peer Review of “Google Trends as a Predictive Tool for COVID-19 Vaccinations in Italy: Retrospective Infodemiological Analysis”

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          Google Trends as a Predictive Tool for COVID-19 Vaccinations in Italy: Retrospective Infodemiological Analysis

          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* 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.

            Author and article information

            Journal
            JMIRx Med
            JMIRx Med
            JMIRxMed
            JMIRx Med
            JMIR Publications (Toronto, Canada )
            2563-6316
            Apr-Jun 2022
            19 April 2022
            19 April 2022
            : 3
            : 2
            : e38724
            Affiliations
            [1 ] College of Science and Engineering Hamad Bin Khalifa University Ar-Rayyan Qatar
            Author information
            https://orcid.org/0000-0001-7389-3274
            Article
            v3i2e38724
            10.2196/38724
            10414398
            92970615-5ff3-4166-84f6-1d3c69dd80cc
            ©Zubair Shah. Originally published in JMIRx Med (https://med.jmirx.org), 19.04.2022.

            This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIRx Med, is properly cited. The complete bibliographic information, a link to the original publication on https://med.jmirx.org/, as well as this copyright and license information must be included.

            History
            : 13 April 2022
            : 13 April 2022
            Categories
            Peer-Review Report
            Peer-Review Report

            covid-19,epidemiology,google trends,infodemiology,infoveillance,italy,public health,sars-cov-2,vaccinations,vaccines,social media analysis,social media

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