A CNN-Based Model of Cross-Immunity to Influenza A(H3N2) Virus: Testing Under "Real-World" Conditions.

A cross-immunity model for influenza A(H3N2) based on convolutional neural networks (CNNs) was developed and validated under temporally structured conditions that mimic real-world forecasting. Antigenic distance was derived from hemagglutination inhibition (HI) titers. The model was trained on WHO data (2011-2023) and tested in a time-split fashion on independent recent data (2022-2024). Hemagglutinin sequences (HA/HA1) were encoded into 3D tensors using five physicochemical indices from AAindex. Two- and three-layer CNN architectures were tested. Performance was evaluated using Accuracy, Sensitivity, Specificity, and Matthews Correlation Coefficient (MCC) with 95% confidence intervals. Validation on the classic Smith's dataset showed high accuracy (Accuracy = 0.9996, MCC = 0.9964), serving as a necessary sanity check. Testing on current data yielded lower but robust results (Accuracy: 0.73-0.81, MCC: 0.48-0.60), reflecting real-world forecasting complexity. ROC analysis confirmed the strong discriminative ability (AUC ≥ 0.805) and good calibration (Brier scores ≤ 0.192). The three-layer CNN demonstrated greater robustness on challenging data. This CNN model is an effective tool for assessing influenza A(H3N2) antigenic distances and holds promise for integration into epidemiological models to aid vaccine strain selection. Further accuracy improvements may arise from modeling the structural impact of amino acid substitutions and polyclonal immune responses.