Prediction of the onset of the RSV epidemic with meteorological data using deep neural networks

Respiratory syncytial virus (RSV) is a contagious virus that infects nearly all children by the age of two and is a leading cause of hospi- talization and mortality among young children. Despite the recent approval of RSV vaccines for elderly and pregnant individuals, immune prophylaxis remains essential for pediatric cases. In Japan, the typical RSV season has shifted, making timely prediction crucial for effective clinical intervention. This study aims to predict the onset of RSV epidemics in Japan using meteorological data, based on the hypothesis that meteorological data affect the spread of RSV. We collected weekly RSV case counts from the Japanese National Institute of Infectious Diseases and daily meteorological data from the Japan Meteorological Agency for the period 2012–2023. Using aggregated weather features (mean, max, min), we constructed a binary classification task to identify the onset of RSV spread. Machine learning models including a sup- port vector machine (SVM), XGBoost, and a deep neural network (DNN) were evaluated. The DNN outperformed other models, achieving the highest F1 score (0.71) and recall (0.83), particularly with a 3-week-ahead prediction horizon. The model demonstrated early detection capability across multiple prefectures, although performance varied geographically, with lower F1 scores in some northern regions. Meteorological data can be effectively utilized to predict the onset of RSV epidemics in Japan. The proposed DNN-based model offers a promising tool for supporting timely prophylactic measures, although fur- ther refinement and integration of additional factors are needed to improve generalizability.