ArticleJournal of medical Internet research2021
Predictive Modeling of Vaccination Uptake in US Counties: A Machine Learning-Based Approach.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 33 citations in OpenAlex.
- Factors influencing COVID-19 vaccine hesitancy in socially vulnerable Latinx communities in North Texas.Human vaccines & immunotherapeutics · 2026Article
- A multi-phase, multi-method assessment of national COVID-19 vaccination performance with equity analysis.Scientific reports · 2026Article
- Modeling Zero-Dose Children in Ethiopia: A Machine Learning Perspective on Model Performance and Predictor Variables.JMIR pediatrics and parenting · 2026Article
- Beyond binary: a machine-learning classification of childhood COVID-19 vaccination intentions using behavioural data.Population health metrics · 2025Article
- Design a model to predict incomplete immunization among Ethiopian children using ensemble machine learning algorithms.Scientific reports · 2025Article
- Evaluating socioeconomic influences on Nipah virus vaccination decisions in Bangladesh through machine learning.Journal of health, population, and nutrition · 2025Article
- Dual decision-making routes for COVID-19 and influenza vaccines uptake in parents: A mixed-methods study.British journal of health psychology · 2025Article
- Development and validation of a clinical prediction tool for non-receipt of updated COVID-19 vaccines.Vaccine · 2025Article
- Disproportionate impact of the COVID-19 pandemic on socially vulnerable communities: the case of Jane and Finch in Toronto, Ontario.Frontiers in public health · 2025Article
- Predicting mobile health clinic utilization for COVID-19 vaccination in South Carolina: A statistical framework for strategic resource allocation.PLOS global public health · 2025Article
- Applying Machine Learning Techniques to Implementation Science.Online journal of public health informatics · 2024Article
- Predicting COVID-19 Vaccination Uptake Using a Small and Interpretable Set of Judgment and Demographic Variables: Cross-Sectional Cognitive Science Study.JMIR public health and surveillance · 2024Article
- Easing anxiety symptoms through leisure activities during social isolation: Findings from nationally representative samples.PloS one · 2024Article
- A protocol for modeling the factors influencing the deployment of the COVID-19 vaccine across African countries.PloS one · 2024Article
- Using COVID-19 Vaccine Attitudes on Twitter to Improve Vaccine Uptake Forecast Models in the United States: Infodemiology Study of Tweets.JMIR infodemiology · 2023Article
- Article
- Predictors of COVID-19 vaccination rate in USA: A machine learning approach.Machine learning with applications · 2022Article
- Vaccination associated with gross domestic product and fewer deaths in countries and regions: A verification study.Medicine · 2022Article
- Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic.Health and technology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough 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.
objectiveThis study aims to determine the association between sociodemographic factors and vaccine uptake across counties in the United States.
methodsSociodemographic 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.
resultsOur 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.
conclusionsOur 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.