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. 2022 Jan 17;12(1):793.
doi: 10.1038/s41598-021-04765-9.

Joint analysis of the intention to vaccinate and to use contact tracing app during the COVID-19 pandemic

Marta Caserotti  1 , Paolo Girardi  2   3 , Alessandra Tasso  4 , Enrico Rubaltelli  1 , Lorella Lotto  1 , Teresa Gavaruzzi  1
Affiliations

Joint analysis of the intention to vaccinate and to use contact tracing app during the COVID-19 pandemic

Marta Caserotti et al. Sci Rep. .

Abstract

Pharmacological and non-pharmacological measures will overlap for a period after the onset of the pandemic, playing a strong role in virus containment. We explored which factors influence the likelihood to adopt two different preventive measures against the COVID-19 pandemic. An online snowball sampling (May-June 2020) collected a total of 448 questionnaires in Italy. A Bayesian bivariate Gaussian regression model jointly investigated the willingness to get vaccinated against COVID-19 and to download the national contact tracing app. A mixed-effects cumulative logistic model explored which factors affected the motivation to adopt one of the two preventive measures. Despite both COVID-19 vaccines and tracing apps being indispensable tools to contain the spread of SARS-CoV-2, our results suggest that adherence to the vaccine or to the national contact tracing app is not predicted by the same factors. Therefore, public communication on these measures needs to take in consideration not only the perceived risk associated with COVID-19, but also the trust people place in politics and science, their concerns and doubts about vaccinations, and their employment status. Further, the results suggest that the motivation to comply with these measurements was predominantly to protect others rather than self-protection.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Marginal distribution and pairwise Spearman’s correlation between Trust in politics and science, Vaccine doubts index, COVID-19 perceived risk, Trust in local Institutions, Self-efficacy score and Conspiracy. Significance test is reported. Spearman’s rank correlation rho test: *p < 0.05, **p < 0.01, ***p < 0.001.
Figure 2
Figure 2
Joint distribution of the likelihood to get a COVID-19 vaccine and to download CTA Immuni.
Figure 3
Figure 3
Adjusted effect of age estimated by a Bayesian Bivariate Gaussian regression model for the position parameter (μ1 and μ2) of the score of likelihood to get a COVID-19 vaccine (a) and to download CTA Immuni (b), and their correlation (rhogit(ρ), (c)).
See this image and copyright information in PMC

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