PDF(Pubmed)
Abstract：
Vaccine hesitancy and acceptance, driven by social influence, is usually explored by most researchers using exhaustive survey-based studies, which investigate public preferences, fundamental values, beliefs, barriers, and drivers through closed or open-ended questionnaires. Commonly used simple statistical tools do not do justice to the richness of this data. Considering the gradual development of vaccine acceptance in a society driven by multiple local/global factors as a compartmental contagion process, we propose a novel methodology where drivers and barriers of these dynamics are detected from survey participants\' responses, instead of heuristic arguments. Applying rigorous natural language processing analysis to the survey responses of participants from India, who are from various socio-demographics, education, and perceptions, we identify and categorize the most important factors as well as interactions among people of different perspectives on COVID-19 vaccines. With a goal to achieve improvement in vaccine perception, we also analyze the resultant behavioral transitions through platforms of unsupervised machine learning and natural language processing to derive a compartmental contagion model from the data. Analysis of the model shows that positive peer influence plays a very important role and causes a bifurcation in the system that reflects threshold-sensitive dynamics.
摘要：
疫苗犹豫和接受，受社会影响的驱使，通常由大多数研究人员使用详尽的调查研究来探索，调查公众的偏好，基本价值观，信仰,障碍,和司机通过封闭式或开放式问卷。常用的简单统计工具对这些数据的丰富性并不公平。考虑到在一个由多个局部/全球因素驱动的社会中，疫苗接受度逐渐发展为一个室性传染过程，我们提出了一种新的方法，从调查参与者的回答中检测这些动态的驱动因素和障碍，而不是启发式的论点。将严格的自然语言处理分析应用于来自印度的参与者的调查回答，他们来自各种社会人口统计学，教育,和感知，我们确定和分类最重要的因素以及不同观点的人对COVID-19疫苗的相互作用。为了实现疫苗感知的改善，我们还通过无监督机器学习和自然语言处理的平台来分析由此产生的行为转变，以从数据中得出一个隔室传染模型。对模型的分析表明，正向同伴影响起着非常重要的作用，并在反映阈值敏感动力学的系统中引起分岔。
