An adaptive weight ensemble approach to forecast influenza activity in the context of irregular seasonality
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Forecasting of influenza activity in tropical and subtropical regions such as Hong Kong is challenging due to irregular seasonality with high variability in the onset of influenza epidemics, and potential summer activity. To overcome this challenge, we develop a diverse set of statistical, machine learning and deep learning approaches to forecast influenza activity in Hong Kong 0-to 8- week ahead, leveraging a unique multi-year surveillance record spanning 34 winter and summer epidemics from 1998-2019. We develop a simple average ensemble (SAE), which is the average of individual forecasts from the top three individual models. We also consider an adaptive weight blending ensemble (AWBE) that allows for dynamic updates of each model contribution based on LASSO regression and uses decaying weights in historical data to capture rapid change in influenza activity. Overall, across all 9 weeks of horizon, all models outperform the baseline constant incidence model, reducing the root mean square error (RMSE) by 23%-29% and weighted interval score (WIS) by 25%-31%. The SAE ensemble only slightly better than individual models, reducing RMSE and WIS by 29%. The AWBE ensemble reduce RMSE by 45% and WIS by 46%, and outperform individual models for forecasts of epidemic trends (growing, flat, descending), and during both winter and summer seasons. Using the post-COVID surveillance data in 2023-2024 as another test period, the AWBE ensemble still reduces RMSE by 32% and WIS by 36%. Our framework contributes to the ensemble forecasting of infectious diseases with irregular seasonality.
Significance statement
In subtropical and tropical regions, irregular influenza seasonality makes accurate forecasting challenging. We test ensemble approaches using diverse statistical, machine learning, and deep learning models based on a unique multi-year surveillance record in Hong Kong. Performance of individual models varies by season and epidemic trend, but simple averaging ensemble cannot improve accuracy. Here we develop an adaptive weight ensemble approach, which updated individual model contributions dynamically. This approach halves the RMSE, outperforms all individual models in different settings and reducing RMSE by one-third even in post-COVID periods. Our method contributes to comparison and benchmarking of models in ensemble forecasts, enhancing the evidence base for synthesizing multiple models in disease forecasting in geographies with irregular influenza seasonality.
