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Phylogenetic-informed graph deep learning to classify dynamic transmission clusters in infectious disease epidemics
Chaoyue Sun1,2, Yanjun Li1,3, Simone Marini4
1NSF Center for Big Learning, University of Florida, Gainesville, FL 32611, United States.
Bioinformatics Advances
|November 12, 2024
Summary
Identifying transmission clusters is vital for public health. DeepDynaTree, a novel deep learning system, accurately predicts pathogen transmission dynamics using phylogenetic trees.
Area of Science:
- Epidemiology
- Computational Biology
- Machine Learning
Background:
- Identifying high-risk groups for pathogen transmission is crucial during outbreaks.
- Understanding dynamic transmission patterns is key for effective public health interventions.
- Phylogenetic tree topology offers insights into population dynamics and transmission.
Purpose of the Study:
- To evaluate limitations of existing tree shape metrics for dynamic transmission clusters.
- To introduce DeepDynaTree, a deep learning system for dynamic classification of transmission clusters.
- To provide a robust tool for characterizing transmission trajectories.
Main Methods:
- Developed a phylogeny-based deep learning system named DeepDynaTree.
- Utilized graph deep learning for dynamic classification of transmission clusters.
- Conducted experiments on simulated epidemic models and HIV epidemic data.
Main Results:
- DeepDynaTree demonstrated effectiveness, robustness, and informativeness in predicting cluster dynamics.
- The system accurately classified dynamic transmission patterns.
- Experiments confirmed the tool's utility in characterizing transmission clusters.
Conclusions:
- DeepDynaTree is a promising tool for transmission cluster characterization.
- The system addresses limitations in understanding transmission trajectory dynamics.
- This approach can aid in strategizing public health interventions for at-risk populations.
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