COVID-19 Vaccine Acceptance in the US and UK in the Early Phase of the Pandemic: AI-Generated Vaccines Hesitancy for Minors, and the Role of Governments
KAIST（韩国科学技术院）；Institute for Basic Science (IBS)（基础科学研究院）；Ewha Womans University（梨花女子大学）；Samsung Medical Center（三星医疗中心）
Gabriel Lima, Meeyoung Cha, Chiyoung Cha, Hyeyoung Hwang
备注：
Published to the Journal of the Korean Data Analysis Society vol. 23 no. 3
This study presents survey results of the public's willingness to get vaccinated against COVID-19 during an early phase of the pandemic and examines factors that could influence vaccine acceptance based on a between-subjects design. A representative quota sample of 572 adults in the US and UK participated in an online survey. First, the participants' medical use tendencies and initial vaccine acceptance were assessed; then, short vignettes were provided to evaluate their changes in attitude towards COVID-19 vaccines. For data analysis, ANOVA and post hoc pairwise comparisons were used. The participants were more reluctant to vaccinate their children than themselves and the elderly. The use of artificial intelligence (AI) in vaccine development did not influence vaccine acceptance. Vignettes that explicitly stated the high effectiveness of vaccines led to an increase in vaccine acceptance. Our study suggests public policies emphasizing the vaccine effectiveness against the virus could lead to higher vaccination rates. We also discuss the public's expectations of governments concerning vaccine safety and present a series of implications based on our findings.
Clarifying Misconceptions in COVID-19 Vaccine Sentiment and Stance Analysis and Their Implications for Vaccine Hesitancy Mitigation: A Systematic Review
University of São Paulo（圣保罗大学）；University of Birmingham（伯明翰大学）；University of Rio de Janeiro State（里约热内卢州立大学）
Can A Society of Generative Agents Simulate Human Behavior and Inform Public Health Policy? A Case Study on Vaccine Hesitancy
Department of Computer Science, Johns Hopkins University（约翰霍普金斯大学计算机科学系）；Institute of Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；Shanghai Qi Zhi Institute（上海启智研究院）；Civil & Systems Engineering, Johns Hopkins University（约翰霍普金斯大学土木与系统工程系）；Department of Computer Science, University of Chicago（芝加哥大学计算机科学系）；Department of Epidemiology, Harvard University（哈佛大学流行病学系）；MIT Media Lab and Department of EECS, Massachusetts Institute of Technology（麻省理工学院媒体实验室和电子工程与计算机科学系）
Knowledge, beliefs, attitudes and perceived risk about COVID-19 vaccine and determinants of COVID-19 vaccine acceptance in Bangladesh
Institute of Statistical Research and Training, University of Dhaka（达卡大学统计研究与培训学院）；Innovations for Poverty Action Bangladesh (IPA-B)（孟加拉国扶贫创新行动组织（IPA-B））；East West University（东西大学）；National Medical College（国立医学院）
Topic Modeling, Clade-assisted Sentiment Analysis, and Vaccine Brand Reputation Analysis of COVID-19 Vaccine-related Facebook Comments in the Philippines
University of Birmingham（伯明翰大学）；De La Salle University（德拉萨大学）
Internet of Everything enabled solution for COVID-19, its new variants and future pandemics: Framework, Challenges, and Research Directions
University of Sindh（信德大学）；Institute of Business Administration, University of Sindh（信德大学工商管理学院）；Nimbus Research Centre（尼姆布斯研究中心）；Munster Technological University（芒斯特理工大学）
2020年，全球爆发了前所未有的SARS-CoV-2，这是一种新型冠状病毒，导致COVID-19大流行，并从根本上改变了我们的生活和工作条件。许多科学家正在不知疲倦地寻找治疗方法和可能的疫苗。此外，各国政府、科研机构和公司正在迅速采取行动，提供资金和开放大容量数据库等资源，以加速旨在解决这一流行病的创新和发现。在本文中，我们开发了一种新的自动化的基于主题的可视化方法，结合先进的数据建模的大型语料库，信息映射和趋势分析，提供一个自上而下和自下而上的浏览和搜索界面，快速发现的主题和研究资源。我们将这种方法应用于两个最近发布的出版物数据集（Dimensions的COVID-19数据集和Allen Institute for AI的CORD-19）。研究结果揭示了一些有趣的信息，包括在社交距离等主题上的努力增加;跨领域举措（例如心理健康和教育）;医学主题的不断发展的研究;以及通过出版物在不同地区展开的病毒轨迹。结果还表明，需要快速和自动启用搜索和浏览大型语料库。我们相信我们的方法将改善未来的大容量可视化和发现系统，但也希望我们的可视化界面目前将帮助科学家，研究人员和公众解决抗击COVID-19大流行的众多问题。
