Spiking Neural Network for Prediction of Future Influenza Strains in the United States

This study investigates the application of LSTM Neural Networks in forecasting viral spread, leveraging CDC's state-level data. It seeks to determine the most precise neural network model for predicting changes in viral strains, focusing on utilizing several different artificial intelligence platforms. After several iterations of each type of model, MSE and MAPE values were used to evaluate performance. LSTM performed better over neural networks, but Spiking Neural Networks (SNN) significantly improved over both methods. Further investigation for SNN models should yield more reliable results.