Predicting influenza in the post-COVID era: assessing LSTM, GRU, and transformer robustness to covariate shift.

Forecasting influenza has become increasingly challenging due to post-COVID disruptions in seasonality and strain circulation. This work compares the performance of Long Short Term Memory Networks (LSTM), Gated Recurrent Unit (GRU), and transformer models in forecasting influenza spread using multivariate epidemiological and environmental data, with a focus on robustness under post-COVID non-stationarity. We compare LSTM, GRU, and transformer architectures within a multivariate deep learning framework using influenza and temperature data from Ontario (2014-2025), with data split into training, validation, and testing periods. Although recurrent models outperform transformers on limited, noisy data, all architectures exhibit marked performance collapse under post-COVID non-stationarity. The GRU and LSTM track pre-COVID seasonal peaks more closely, yet both substantially under-estimate the post-COVID resurgence, indicating that none of the models generalize across the regime shift. These findings position our study as a diagnostic of how architectural inductive biases break down under covariate shift. Furthermore, this manuscript assesses how the COVID-19 pandemic affected the accuracy and performance of machine learning algorithms and notes the integration of transfer learning and attention mechanisms to improve model performance.