A GRU-CNN Hybrid Model for Forecasting Influenza-Like Illnesses

Music City Center 

Influenza infection remains a major cause of morbidity and mortality in the United States and worldwide. According to the Centers for Disease Control and Prevention (CDC), between 2010 and 2020, influenza was responsible for 9 to 41 million cases of illness annually, resulting in 140,000 to 710,000 hospitalizations and 12,000 to 52,000 deaths. Beyond its direct health impact, influenza imposes a substantial economic burden on the U.S.

This study integrates influenza-like illness (ILI) data from the CDC with air quality metrics from the U.S. Environmental Protection Agency (EPA) and weather data from the National Oceanic and Atmospheric Administration (NOAA). We developed a novel GRU-CNN hybrid deep learning model to forecast influenza trends using these environmental and meteorological inputs. The model incorporates Yeo-Johnson scaling, self-normalizing activations, and adaptive learning rate scheduling to enhance accuracy and stability. We evaluated the hybrid model's performance and compared it to traditional INGARCH and INGARCHX models. The GRU-CNN approach demonstrated superior predictive accuracy while maintaining computational efficiency, underscoring its potential as a powerful tool for real-time influenza surveillance.

Deep learning

Influenza-like illness

INGARCH

INGARCHX

LSTM

CO,NO2, O3, PM2.5, PM10, SO2 

Section on Statistics in Epidemiology