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This study provides the bibliometric analysis of publications addressing the COVID-19 pandemic and preventive measures to overcome it. This study aims to analyze, systematize, and build clusters of world schools of thought that changed their research directions in connection with the COVID-19 pandemic. The relevance of solving the scientific problem is urgent to quickly restore the economy, education, tourism, and other spheres of society affected by the pandemic. The authors emphasized that vaccination is one of the effective ways to reduce COVID-19 morbidity. Therefore, the study sample was generated with articles indexed by keywords “COVID-19” and «vaccination» in the Web of Science and Scopus databases. The study period covers 2020-2021. To operate with the most relevant publications, the study sample was limited by the English publication language and subject areas, excluding the publications in the categories of medicine and pharmacology. The case study involved the VOSviewer software, Web of Science, and Scopus database analysis tools in analyzing the scientific background on the issue of trust in the vaccination campaign. The visualization of findings was conducted using the VOSviewer software tools. The obtained results showed most of the work was published by the scholars of American, English, Chinese, German and Italian affiliations. The study identified at least 10 research directions on the investigated topic: the reasons for differentiating the intentions to be vaccinated; attitudes towards vaccinations depending on gender, age, and social status; forecasting different recovering scenarios; consequences of misinformation and fight against misinformation; effectiveness of social pressure on the population; the role of social networks; sufficiency of using personal protective equipment; the self-responsibility in creating collective immunity; the need medical staff visits; testing the effectiveness of the vaccine, etc. The findings of the bibliometric analysis could be useful for further empirical studies to find cause-and-effect relationships and mathematical modeling of the reasons for vaccination refusal and predicting different pandemic scenarios. |
