A hybrid model for influenza forecasting in Xinjiang: adapting to complex seasonality and post-COVID-19 structural...

Influenza forecasting in Xinjiang is critical but hindered by unique dual challenges: complex, multi-modal routine seasonality and extreme post-COVID-19 structural breaks. This study develops a two-stage hybrid model that fuses local epidemiological features to provide accurate forecasts where general-purpose models may fail.

We utilize data from 2011 to 2023 provided by the Disease Prevention and Control Center of Xinjiang Production and Construction Corps, Urumqi, Xinjiang, China, stratifying it into pre-pandemic and COVID-19-affected periods to test model performance against routine complexity and major structural breaks. We propose a two-stage Long Short-Term Memory-Gradient Boosting Regression (LSTM-GBR) model that fuses an epidemic trend baseline, extracted from historical cases by an LSTM, with engineered virological and demographic features via a GBR. We systematically compare its performance across key metrics against a breadth of benchmarks, including classical statistical models, automated forecasting tools, and state-of-the-art pre-trained foundation models.

The proposed LSTM-GBR framework demonstrates superior performance and yields the lowest error. It is the only model to accurately resolve the complex bimodal seasonality in the pre-pandemic test set. Furthermore, it is the only model to successfully anticipate and forecast the extreme 2023 post-COVID-19 rebound. All baseline models, including state-of-the-art foundation models, fail to predict these critical dynamics.

The proposed LSTM-GBR model provides a validated, robust, high-accuracy forecasting tool specifically for Xinjiang. It successfully addresses the failures of general-purpose models, which are unable to capture the region's complex local dynamics and emergent structural breaks. This localized feature-fusion approach is essential to strengthen public health surveillance in Xinjiang.