This paper is in the following e-collection/theme issue:

Public (e)Health, Digital Epidemiology and Public Health Informatics (646) Machine Learning (1401) Vaccination and Immunization in the Digital Age (347) Outbreak and Pandemic Preparedness and Management (1679) Infectious Diseases (non-STD/STI) (1490) COVID-19 Vaccination (128) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1484) Innovative Methods in Public Health and Surveillance (307) Health Care Quality and Health Services Research (252) JMIR Theme Issue: COVID-19 Special Issue (2394)

Published on in Vol 23, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33231, first published .
Predictive Modeling of Vaccination Uptake in US Counties: A Machine Learning–Based Approach

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

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

Authors of this article:

Queena Cheong1 Author Orcid Image ;   Martin Au-yeung2 Author Orcid Image ;   Stephanie Quon3 Author Orcid Image ;   Katsy Concepcion2 Author Orcid Image ;   Jude Dzevela Kong4 Author Orcid Image
Article Authors Cited by (11) Tweetations (11) Metrics

Journals

  1. Wang L, Chien T, Lin J, Chou W. Vaccination associated with gross domestic product and fewer deaths in countries and regions. Medicine 2022;101(4):e28619 View
  2. Oyewola D, Dada E, Misra S. Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic. Health and Technology 2022;12(6):1277 View
  3. Osman S, Sabit A. Predictors of COVID-19 vaccination rate in USA: A machine learning approach. Machine Learning with Applications 2022;10:100408 View
  4. Obaido G, Ogbuokiri B, Swart T, Ayawei N, Kasongo S, Aruleba K, Mienye I, Aruleba I, Chukwu W, Osaye F, Egbelowo O, Simphiwe S, Esenogho E. An Interpretable Machine Learning Approach for Hepatitis B Diagnosis. Applied Sciences 2022;12(21):11127 View
  5. Demsash A, Chereka A, Walle A, Kassie S, Bekele F, Bekana T, Enyew E. Machine learning algorithms’ application to predict childhood vaccination among children aged 12–23 months in Ethiopia: Evidence 2016 Ethiopian Demographic and Health Survey dataset. PLOS ONE 2023;18(10):e0288867 View
  6. Sigalo N, Awasthi N, Abrar S, Frias-Martinez V. Using COVID-19 Vaccine Attitudes on Twitter to Improve Vaccine Uptake Forecast Models in the United States: Infodemiology Study of Tweets. JMIR Infodemiology 2023;3:e43703 View
  7. McGlacken T, Codd M. Comparison, by Country, of the Uptake of COVID-19 Vaccination by Health Care Workers in the EU/EEA, January – June 2021. The Open COVID Journal 2023;3(1) View
  8. Vike N, Bari S, Stefanopoulos L, Lalvani S, Kim B, Maglaveras N, Block M, Breiter H, Katsaggelos A. 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 2024;10:e47979 View
  9. Huguet N, Chen J, Parikh R, Marino M, Flocke S, Likumahuwa-Ackman S, Bekelman J, DeVoe J. Applying Machine Learning Techniques to Implementation Science. Online Journal of Public Health Informatics 2024;16:e50201 View
  10. Cheong Q, Kazanjian A, Puyat J, Arnout B. Easing anxiety symptoms through leisure activities during social isolation: Findings from nationally representative samples. PLOS ONE 2024;19(6):e0303585 View
  11. Obaido G, Mienye I, Egbelowo O, Emmanuel I, Ogunleye A, Ogbuokiri B, Mienye P, Aruleba K. Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects. Machine Learning with Applications 2024;17:100576 View