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.

UNSTRUCTURED Biomedical engineering is a rapidly growing field that combines principles from engineering and the life sciences to address medical and biological problems. However, there is a significant gap between health and engineering in biomedical engineering education, which can lead to a lack of understanding and collaboration between health and engineering professionals, limiting the development and implementation of practical solutions. To address this gap, there is a need for more integration of health and engineering in biomedical engineering programs concerning local context. By integrating these disciplines more closely with local environments, regulations, challenges, and barriers, biomedical engineering students can better understand the intersection between medicine and engineering in their current environment and how engineers can use their skill set to improve health care and medical technology.

BACKGROUND While the COVID-19 pandemic has left an unprecedented impact globally, countries such as the United States of America have reported the most significant incidence of COVID-19 cases worldwide. Within the U.S., various sociodemographic factors have played an essential role in the creation of regional disparities. Regional disparities have resulted in the unequal spread of disease between U.S. counties, underscoring the need for efficient and accurate predictive modelling strategies to inform public health officials and reduce the burden on healthcare systems. Furthermore, despite the widespread accessibility of COVID-19 vaccines across the U.S., vaccination rates have become stagnant, necessitating predictive modelling to identify important factors impacting vaccination uptake. OBJECTIVE To determine the association between sociodemographic factors and vaccine uptake across counties in the U.S. METHODS Sociodemographic data on fully vaccinated and unvaccinated individuals were sourced from several online databases, such as the U.S. Centre for Disease Control and U.S. Census Bureau COVID-19 Site. Machine learning analysis was performed using XGBoost and sociodemographic data. RESULTS Our model predicted COVID-19 vaccination uptake across U.S. countries with 59% accuracy. In addition, it identified location, education, ethnicity, and income as the most critical sociodemographic features in predicting vaccination uptake in U.S. counties. Lastly, the model produced a choropleth demonstrating areas of low and high vaccination rates, which can be used by healthcare 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 rate across counties in the U.S. and if leveraged appropriately can assist policy makers and public health officials to understand vaccine uptake rates and craft policies to improve them.