Speaker
Description
Norovirus is a primary agent of acute gastroenteritis in all age groups, with young children under five being particularly vulnerable. Due to the virus’s pronounced seasonal behavior, forecasting its detection rate based on climatic factors is essential. To characterize the periodic relationship between climate variables and norovirus detection rates, wavelet coherence analysis was applied. The phase shift in the one-year cycle was examined to identify temporal changes in seasonal behavior. To further capture long-term nonlinear trends, generalized additive models (GAMs) were used to estimate the underlying trend in the detection rate. Based on the GAM-derived trend, the data were adjusted to reduce long-term variability and emphasize seasonal signals. The model was trained using data from January 2007 to June 2019 and tested using data from June 2019 to December 2020. Weekly detection rates were predicted using four machine learning models, incorporating wavelet-derived features that reflect time-varying seasonal characteristics. The inclusion of wavelet analysis and trend adjustment improved prediction accuracy by approximately 10–15% compared with models using the original climate variables alone.
