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Predicting influenza in the post-COVID era: assessing LSTM; GRU; and transformer robustness to covariate shift
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DOI：10.3389/frai.2026.1886896.png)
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Abstract
En 中文
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.
Keywords:
influenza
transformer
attention mechanism
regime shift
back propagation
GRU (Gated Recurrent Unit)
LSTM (Long Short Term Memory Networks)
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