Predictive Modeling of Vaccination Uptake in US Counties: A Machine Learning–Based Approach

Abstract

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.

Vaccination associated with gross domestic product and fewer deaths in countries and regions

“…Our findings are consistent with those done in the United States. [ 62 ] As a result, enough people need to have access or be willing to receive the vaccine to achieve herd immunity. [ 63 ] Nonetheless, previous literature has indicated disparities in vaccination rates between sociodemographic groups, and such factors play a substantial role in the likelihood of seeking vaccination (e.g., those with lower education and income levels [ 64 , 65 ] and black individuals [ 66 ] are less likely to get vaccinated).…”

“…The association is weak between vaccinations and CNCC IP based on the argument that vaccinations are limited to protection against milder infection [2] . The spectrum of COVID-19 ranges from mild to critical [62] . Although most individuals suffer from a mild form of the disease, [72] more than half of the patients who died were admitted to the HDU and ICU, [62] suffering from the critical form of illness.…”

Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic

“…Cheong et al [23] used machine learning techniques to examine socioeconomic data from a range of online sources, including the US CDC and the US Census Bureau COVID-19 Site. Using XGBoost and socioeconomic data, a machine learning study was done.…”

Explainable Machine Learning Approaches to Assess the COVID-19 Vaccination Uptake: Social, Political, and Economic Aspects

“…Jayasurya et al and others [56][57][58] conducted sentiment analysis to understand the public sentiments and opinions toward the COVID-19 vaccines by using social media data. Cheong et al [59] utilized the XGBoost regression model to explore the association between sociodemographic factors and vaccination rate across US counties. They have used XGBoost's builtin feature importance along with permutation and SHAP feature importances to find the important predictors.…”