ArticleAmerican journal of epidemiology2022
Using Machine Learning Techniques and National Tuberculosis Surveillance Data to Predict Excess Growth in Genotyped Tuberculosis Clusters.
Article in American journal of epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- Characteristics of Plausible Source Cases Responsible for Recent Mycobacterium tuberculosis Transmission, United States, 2018-2022.Emerging infectious diseases · 2026Article
- Identification of tuberculosis transmission hotspots in urban China using surveillance data: a machine learning approach based on genomic and spatial analysis.BMC infectious diseases · 2026Article
- Machine Learning-based Prediction of Active Tuberculosis in People With HIV Using Clinical Data.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2025Article
- Artificial Intelligence in Bacterial Infections Control: A Scoping Review.Antibiotics (Basel, Switzerland) · 2025Review
- Model-Based Analysis of Impact, Costs, and Cost-effectiveness of Tuberculosis Outbreak Investigations, United States.Emerging infectious diseases · 2025Article
- Early prediction of Mycobacterium tuberculosis transmission clusters using supervised learning models.Scientific reports · 2024Article
- Identification of growing tuberculosis incidence clusters in a region with a decrease in tuberculosis prevalence in Moscow (2000-2019).Journal of global health · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
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 accumulating cases above expected growth within a 1-year follow-up period. We developed a model to predict which clusters are likely to grow on a training and testing data set that was generalizable to a validation data set. Our model showed that characteristics of clusters were more important than the social, demographic, and clinical characteristics of the patients in those clusters. For instance, the time between cases before unexpected growth was identified as the most important of our predictors. A faster accumulation of cases increased the probability of excess growth being predicted during the follow-up period. We have demonstrated that combining the characteristics of clusters and cases with machine learning can add to existing tools to help prioritize which clusters may benefit most from public health interventions. For example, consideration of an entire cluster, not only an individual patient, may assist in interrupting ongoing transmission.
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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.