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

Formative Evaluation of Digital Health Interventions (5168) Surveillance Systems (683) Machine Learning (3197) Infectious Diseases (non-STD/STI) (1933)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42832, first published .
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Predicting Measles Outbreaks in the United States: Evaluation of Machine Learning Approaches

Predicting Measles Outbreaks in the United States: Evaluation of Machine Learning Approaches

Authors of this article:

Boshu Ru1 Author Orcid Image ;   Stephanie Kujawski2 Author Orcid Image ;   Nelson Lee Afanador2 Author Orcid Image ;   Richard Baumgartner2 Author Orcid Image ;   Manjiri Pawaskar2 Author Orcid Image ;   Amar Das2 Author Orcid Image
Article Authors Cited by (6) Tweetations (3) Metrics

Journals

  1. Kujawski S, Ru B, Afanador N, Conway J, Baumgartner R, Pawaskar M. Prediction of measles cases in US counties: A machine learning approach. Vaccine 2024;42(26):126289 View
  2. Baiasu A, Rotaru-Zavaleanu A, Boldea A, Ruscu M, Serbanescu M, Radu L. Bridging Traditional Modeling and Artificial Intelligence in Measles Epidemiology: Methods, Applications, and Future Directions—A Narrative Review. Journal of Clinical Medicine 2026;15(9):3242 View
  3. Cammarota D, Mori D, da Silva Sales F, Galvão I, da Silva Gomes A, Prado F, Wilk da Silva R, Palasio R, de Carvalho Lucas P, Carvalhanas T, Yu A. Estimating measles reintroduction risk in São Paulo using machine learning. Public Health 2026;258:106417 View
  4. Emegano D, Ozsahin I, Uzun Ozsahin P, Emeje P. Measles Prediction Modeling using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). Sakarya University Journal of Science 2026;(Advanced Online Publication):465 View

Books/Policy Documents

  1. Kulkarni J, Verma P, Laddha S. Data Science and Applications. View
  2. Mbunge E. Software Engineering: Emerging Trends and Practices in System Development. View