Prediksi Kompleksitas Mutasi Virus Influenza dalam Pengembangan Vaksin yang Efektif untuk Anak: Analisis - Birgitta Priscilla; Benedictus; Bernie Endyarni Medise (Cermin Dunia Kedokteran, 2025)
Influenza virus infection is common in populations of all ages. Vaccination is the most effective measure to prevent infection. However, influenza viruses are prone to antigenic drift, causing the virus to mutate within a few months. Predicting influenza virus evolution plays a crucial role in ensuring the protective effect of vaccines, allowing for the selection of the appropriate vaccine type. Stacking models, convolutional neural network (CNN) models, Gaussian processes vector autoregressive models, and susceptible-exposed-infectious-removed (SEIR) models can predict influenza virus antigenic variants. Various modern models and approaches, such as influenza antigenic variants (IAV)-CNN models, sequence-based antigenic distance approach (SBA), and ensemble of nonlinear regression models, have been used to improve the efficacy and relevance of vaccination strategies.