Sammut Fiona, Suda David, Caruana Mark Anthony, Bogolyubova Olga
Department of Statistics & Operations Research, University of Malta, Msida, Malta.
Department of Psychology, University of Malta, Msida, Malta.
Heliyon. 2023 Aug 6;9(8):e18903. doi: 10.1016/j.heliyon.2023.e18903. eCollection 2023 Aug.
This study was conducted to determine the predictors of COVID-19 vaccination attitudes across multiple waves in seven countries geographically spread across the European continent, using data from a COVID-19 survey provided by the Massachusetts Institute of Technology COVID-19. Facebook users from across the globe participated in this survey which collected information on their knowledge of COVID-19, attitudes towards risk and available information, and their willingness or lack thereof to take the vaccine. In this secondary data analysis study, neural networks were used with special attention given to the importance of the predictors of COVID-19 vaccination attitudes. Perception of social norms regarding COVID-19 vaccination was found to be the most important predictor of vaccine acceptance. Country of residence and wave of data collection were among the important predictors, with different patterns for each country emerging across different waves. Other strong predictors included attitudes towards masks and mask wearing; attitudes towards the influenza vaccine; distrust in government health authorities and scientists; and level of knowledge of existing treatments for COVID-19. The results of this study can inform effective public health prevention and intervention efforts against infectious diseases.
本研究旨在利用麻省理工学院新冠疫情调查提供的新冠疫情数据,确定欧洲大陆七个地理位置分散的国家多轮新冠疫苗接种态度的预测因素。来自全球各地的脸书用户参与了这项调查,该调查收集了他们对新冠疫情的了解、对风险和可用信息的态度,以及他们接种疫苗的意愿或不愿意程度。在这项二次数据分析研究中,使用了神经网络,并特别关注新冠疫苗接种态度预测因素的重要性。发现对新冠疫苗接种社会规范的认知是疫苗接受度的最重要预测因素。居住国和数据收集轮次是重要预测因素之一,不同国家在不同轮次呈现出不同模式。其他强有力的预测因素包括对口罩及戴口罩的态度;对流感疫苗的态度;对政府卫生当局和科学家的不信任;以及对新冠现有治疗方法的了解程度。本研究结果可为针对传染病的有效公共卫生预防和干预措施提供参考。