# Cultural-aware Machine Learning based Analysis of COVID-19 Vaccine Hesitancy

**Authors:** Raed Alharbi, Sylvia Chan-Olmsted, Huan Chen, My T. Thai  
**arXiv:** [2304.06953](https://arxiv.org/abs/2304.06953)  
**Date:** 2023-04-14  
**Summary page:** https://www.summarizepaper.com/en/paper/2304.06953v1/

## Key points

- COVID-19 pandemic has caused significant disruptions to daily life
- Vaccination is one of the most effective methods for controlling its spread
- Vaccine hesitancy is a major obstacle to achieving widespread adoption
- Understanding who is hesitant and why they are hesitant is crucial for designing successful vaccination campaigns and controlling the pandemic
- Factors contributing to vaccine hesitancy are cultural in nature
- A culture-aware machine learning (ML) model was designed by Raed Alharbi's team to predict vaccination willingness based on new data collection
- Advanced AI explainers such as Probabilistic Graphical Model (PGM) and Shapley Additive Explanations (SHAP) were used to analyze the most important features contributing to the ML model's predictions.
- Cultural characteristics such as religion and ethnic affiliation significantly impact vaccine adoption decisions among Hispanic and African American communities.
- Asian communities are more influenced by vaccine trust and approval.
- Cultural characteristics, rumors, and political affiliation were associated with increased vaccine rejection.
- This research provides valuable insights into understanding vaccine hesitancy from a cultural perspective
- It can inform future vaccination campaigns during pandemics.
- Addressing these cultural factors in designing vaccination campaigns can increase vaccine adoption rates and effectively control the spread of infectious diseases like COVID-19.

## Summary

The COVID-19 pandemic has caused significant disruptions to daily life, and vaccination remains one of the most effective methods for controlling its spread. However, vaccine hesitancy is a major obstacle to achieving widespread adoption. Understanding who is hesitant and why they are hesitant is crucial for designing successful vaccination campaigns and controlling the pandemic. Unfortunately, there are many factors that contribute to vaccine hesitancy, particularly from a cultural perspective. To address this issue, a team of researchers led by Raed Alharbi designed a novel culture-aware machine learning (ML) model based on new data collection to predict vaccination willingness. They used advanced AI explainers such as the Probabilistic Graphical Model (PGM) and Shapley Additive Explanations (SHAP) to analyze the most important features contributing to the ML model's predictions. Their findings revealed that cultural characteristics such as religion and ethnic affiliation significantly impact vaccine adoption decisions among Hispanic and African American communities. In contrast, Asian communities are more influenced by vaccine trust and approval. Additionally, cultural characteristics, rumors, and political affiliation were associated with increased vaccine rejection. This research provides valuable insights into understanding vaccine hesitancy from a cultural perspective and can inform future vaccination campaigns during pandemics. By addressing these cultural factors in designing vaccination campaigns, public health officials can increase vaccine adoption rates and effectively control the spread of infectious diseases like COVID-19.