<ul><li>A hybrid CNN-LSTM deep learning model was developed to predict COVID-19 severity using spike protein sequences and clinical metadata from South American patients.</li><li>The model achieved an F1 score of 82.92%, ROC-AUC of 0.9084, precision of 83.56%, and recall of 82.85%, indicating robust classification performance.</li><li>Training of the model stabilized at 85% accuracy with minimal overfitting, demonstrating its effectiveness in predicting disease severity.</li><li>The study suggests potential associations between viral genetics (prevalent lineages and clades) and clinical outcomes, highlighting the role of AI in genomic surveillance for public health.</li></ul>

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data

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