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

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.

Easing anxiety symptoms through leisure activities during social isolation: Findings from nationally representative samples.

Public health interventions implemented during the COVID-19 pandemic may exacerbate anxiety symptoms for many. We conducted this study to better understand the role of leisure activity in promoting mental wellness during times of social isolation and reduced access to recreation facilities and mental health support services. We analyzed nationally representative survey data collected by Statistics Canada as part of the Canadian Perspectives Survey Series (CPSS) during May 4-10 (CPSS 2) and July 20 to 26, 2020 (CPSS 4). Data related to leisure activity and anxiety symptoms as measured by a score of more than 10 on the General Anxiety Disorder scale were examined using descriptive and log-binomial regression analyses. Survey sampling weights were applied in all analyses, and regression results were adjusted for sociodemographic characteristics. Exercise and communication with friends and loved ones were the most frequently reported leisure activity. Prevalence of moderate to severe anxiety symptoms reported by participants was lower in CPSS 4 compared to CPSS 2. Results of adjusted log-binomial regression analyses revealed lower prevalence of moderate to severe anxiety symptoms in those who engaged in exercise and communication, while those who meditated exhibited higher prevalence. In conclusion, leisure activities, such as exercise and communication with loved ones, can promote mental wellness. Future research should clarify the role of meditation for mental wellness promotion during periods of social isolation.