Article
Using Machine Learning Techniques and National Tuberculosis Surveillance Data to Predict Excess Growth in Genotyped Tuberculosis Clusters.
American journal of epidemiology - 20 Oct 2022
Althomsons Sandy P, Winglee Kathryn, Heilig Charles M, Talarico Sarah, Silk Benjamin, Wortham Jonathan, Hill Andrew N, Navin Thomas R
Abstract excerpt
The early identification of clusters of persons with tuberculosis (TB) that will grow to become outbreaks creates an opportunity for intervention in preventing future TB cases. We used surveillance data (2009-2018) from the United States, statistically derived definitions of unexpected growth, and machine-learning techniques to predict which clusters of genotype-matched TB cases are most likely to continue...
Read the complete abstract on PubMedTopics
Share this publication in a Topic to start or enrich a Post.