TY  - JOUR
AU  - Cheong, Queena
AU  - Au-yeung, Martin
AU  - Quon, Stephanie
AU  - Concepcion, Katsy
AU  - Kong, Jude Dzevela
PY  - 2021
DA  - 2021/11/25
TI  - Predictive Modeling of Vaccination Uptake in US Counties: A Machine Learning–Based Approach
JO  - J Med Internet Res
SP  - e33231
VL  - 23
IS  - 11
KW  - COVID-19
KW  - vaccine
KW  - public health
KW  - machine learning
KW  - XGBoost
KW  - SARS-CoV-2
KW  - sociodemographic factors
KW  - United States
KW  - sociodemographic
KW  - prediction
KW  - model
KW  - uptake
AB  - Background: Although the COVID-19 pandemic has left an unprecedented impact worldwide, countries such as the United States have reported the most substantial incidence of COVID-19 cases worldwide. Within the United States, various sociodemographic factors have played a role in the creation of regional disparities. Regional disparities have resulted in the unequal spread of disease between US counties, underscoring the need for efficient and accurate predictive modeling strategies to inform public health officials and reduce the burden on health care systems. Furthermore, despite the widespread accessibility of COVID-19 vaccines across the United States, vaccination rates have become stagnant, necessitating predictive modeling to identify important factors impacting vaccination uptake. Objective: This study aims to determine the association between sociodemographic factors and vaccine uptake across counties in the United States. Methods: Sociodemographic data on fully vaccinated and unvaccinated individuals were sourced from several online databases such as the US Centers for Disease Control and Prevention and the US Census Bureau COVID-19 Site. Machine learning analysis was performed using XGBoost and sociodemographic data. Results: Our model predicted COVID-19 vaccination uptake across US counties with 62% accuracy. In addition, it identified location, education, ethnicity, income, and household access to the internet as the most critical sociodemographic features in predicting vaccination uptake in US counties. Lastly, the model produced a choropleth demonstrating areas of low and high vaccination rates, which can be used by health care authorities in future pandemics to visualize and prioritize areas of low vaccination and design targeted vaccination campaigns. Conclusions: Our study reveals that sociodemographic characteristics are predictors of vaccine uptake rates across counties in the United States and, if leveraged appropriately, can assist policy makers and public health officials to understand vaccine uptake rates and craft policies to improve them. 
SN  - 1438-8871
UR  - https://www.jmir.org/2021/11/e33231
UR  - https://doi.org/10.2196/33231
UR  - http://www.ncbi.nlm.nih.gov/pubmed/34751650
DO  - 10.2196/33231
ID  - info:doi/10.2196/33231
ER  -