Cultural-aware Machine Learning based Analysis of COVID-19 Vaccine Hesitancy
This work addresses vaccine hesitancy for public health policymakers by providing insights to design effective vaccination campaigns, though it is incremental as it applies existing ML methods to a new cultural context.
The researchers tackled the problem of understanding COVID-19 vaccine hesitancy by developing a culture-aware machine learning model to predict vaccination willingness, revealing that cultural characteristics like religion and ethnic affiliation most impact Hispanic and African American communities, while vaccine trust affects Asian communities, with cultural factors, rumors, and political affiliation linked to increased rejection.
Understanding the COVID-19 vaccine hesitancy, such as who and why, is very crucial since a large-scale vaccine adoption remains as one of the most efficient methods of controlling the pandemic. Such an understanding also provides insights into designing successful vaccination campaigns for future pandemics. Unfortunately, there are many factors involving in deciding whether to take the vaccine, especially from the cultural point of view. To obtain these goals, we design a novel culture-aware machine learning (ML) model, based on our new data collection, for predicting vaccination willingness. We further analyze the most important features which contribute to the ML model's predictions using advanced AI explainers such as the Probabilistic Graphical Model (PGM) and Shapley Additive Explanations (SHAP). These analyses reveal the key factors that most likely impact the vaccine adoption decisions. Our findings show that Hispanic and African American are most likely impacted by cultural characteristics such as religions and ethnic affiliation, whereas the vaccine trust and approval influence the Asian communities the most. Our results also show that cultural characteristics, rumors, and political affiliation are associated with increased vaccine rejection.