A Multimodal Machine Learning Framework Integrating Genomic, Epidemiological, Environmental, and Clinical Imaging Data for Accelerated Measles Detection and Transmission Inference
Read the original on arXiv Machine Learning →The study presents a multimodal machine‑learning framework that combines genomic sequences, epidemiological indicators, wastewater surveillance, and clinical rash images to detect measles and infer transmission pathways. In a three‑stage pipeline, the authors achieved high accuracy in classifying transmission linkage (AUC 0.924, F1 0.912), moderate success in predicting outbreak risk (AUC 0.707), and near‑perfect recall for aligning genomic and imaging embeddings (Recall@1 0.985). The results highlight the complementary strengths of genomic data for linkage prediction and wastewater data for outbreak‑risk forecasting.
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