A Multimodal Machine Learning Framework Integrating Genomic, Epidemiological, Environmental, and Clinical Imaging Data for Accelerated Measles Detection and Transmission Inference
Abstract
Background: Measles resurged in the United States during 2025-2026, with more than 4,600 reported cases. Timely detection and transmission inference require integrated genomic, epidemiological, environmental, and clinical data. We developed a multimodal machine learning (ML) framework integrating genomic sequences, epidemiological indicators, wastewater surveillance, and clinical rash images. Methods: We evaluated a three-stage pipeline using public data. Stage 1 classified transmission linkage among 903 sequence pairs from the 2021 Ohio-Arizona-Washington outbreak using k-mer TF-IDF-SVD embeddings and pairwise single-nucleotide polymorphism (SNP) distances with leave-one-cluster-out cross-validation. Stage 2 predicted state-week outbreak risk using logistic regression and XGBoost with wastewater, mobility, and vaccination features under leave-one-year-out cross-validation. Stage 3 used BiomedCLIP ViT-B/16 embeddings and a fine-tuned multilayer perceptron for rash classification on MSLD v2.0 (755 images, 6 classes). InfoNCE contrastive learning aligned genomic and imaging embeddings. Results: Transmission-linkage classification achieved AUC 0.924 and F1 0.912 with logistic regression. Outbreak-risk prediction achieved AUC 0.707 with logistic regression across 2025-2026 leave-one-year-out folds. Contrastive alignment achieved Recall@1 = 0.985 on held-out paired data. Ablation showed genomic features dominated transmission-linkage prediction (genomic-only AUC 0.983), whereas wastewater positivity was the strongest outbreak-risk predictor (importance 0.402). Conclusions: Multimodal ML integrating genomic, epidemiological, wastewater, and clinical imaging data can support measles surveillance and transmission inference using public data. Prospective validation with paired clinical-genomic data, expanded wastewater coverage, and real-world workflows is needed before deployment.
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BibTeX
@misc{li2026multimodal,
title = {{A Multimodal Machine Learning Framework Integrating Genomic, Epidemiological, Environmental, and Clinical Imaging Data for Accelerated Measles Detection and Transmission Inference}},
author = {Jiada Li},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.32996},
eprint = {2610.32996},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.32996},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A Multimodal Machine Learning Framework Integrating Genomic, Epidemiological, Environmental, and Clinical Imaging Data for Accelerated Measles Detection and Transmission Inference
AU - Jiada Li
PY - 2026
DA - 2026/10/05
PB - arXiv
DO - 10.48550/arxiv.2610.32996
UR - https://arxiv.org/abs/2610.32996
AB - Background: Measles resurged in the United States during 2025-2026, with more than 4,600 reported cases. Timely detection and transmission inference require integrated genomic, epidemiological, environmental, and clinical data. We developed a multimodal machine learning (ML) framework integrating genomic sequences, epidemiological indicators, wastewater surveillance, and clinical rash images. Methods: We evaluated a three-stage pipeline using public data. Stage 1 classified transmission linkage among 903 sequence pairs from the 2021 Ohio-Arizona-Washington outbreak using k-mer TF-IDF-SVD embeddings and pairwise single-nucleotide polymorphism (SNP) distances with leave-one-cluster-out cross-validation. Stage 2 predicted state-week outbreak risk using logistic regression and XGBoost with wastewater, mobility, and vaccination features under leave-one-year-out cross-validation. Stage 3 used BiomedCLIP ViT-B/16 embeddings and a fine-tuned multilayer perceptron for rash classification on MSLD v2.0 (755 images, 6 classes). InfoNCE contrastive learning aligned genomic and imaging embeddings. Results: Transmission-linkage classification achieved AUC 0.924 and F1 0.912 with logistic regression. Outbreak-risk prediction achieved AUC 0.707 with logistic regression across 2025-2026 leave-one-year-out folds. Contrastive alignment achieved Recall@1 = 0.985 on held-out paired data. Ablation showed genomic features dominated transmission-linkage prediction (genomic-only AUC 0.983), whereas wastewater positivity was the strongest outbreak-risk predictor (importance 0.402). Conclusions: Multimodal ML integrating genomic, epidemiological, wastewater, and clinical imaging data can support measles surveillance and transmission inference using public data. Prospective validation with paired clinical-genomic data, expanded wastewater coverage, and real-world workflows is needed before deployment.
ER -