WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong.
Nat Commun. 2024 Oct 4;15(1):8625. doi: 10.1038/s41467-024-52504-1.
Forecasting influenza activity in tropical and subtropical regions, such as Hong Kong, is challenging due to irregular seasonality and high variability. We develop a diverse set of statistical, machine learning, and deep learning approaches to forecast influenza activity in Hong Kong 0 to 8 weeks ahead, leveraging a unique multi-year surveillance record spanning 32 epidemics from 1998 to 2019. We consider a simple average ensemble (SAE) of the top two individual models, and develop an adaptive weight blending ensemble (AWBE) that dynamically updates model contribution. 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% for 8-week ahead forecasts. The SAE model performed similarly to individual models, while the AWBE model reduces RMSE by 52% and WIS by 53%, outperforming individual models for forecasts in different epidemic trends (growth, plateau, decline) and during both winter and summer seasons. Using the post-COVID data (2023-2024) as another test period, the AWBE model still reduces RMSE by 39% and WIS by 45%. Our framework contributes to comparing and benchmarking models in ensemble forecasts, enhancing evidence for synthesizing multiple models in disease forecasting for geographies with irregular influenza seasonality.
在香港等热带和亚热带地区预测流感活动具有挑战性，因为季节性不规则且变化性高。我们开发了一系列统计、机器学习和深度学习方法，以预测香港 0 到 8 周内的流感活动，利用了 1998 年至 2019 年跨越 32 次流行的独特多年监测记录。我们考虑了两个最佳个体模型的简单平均集成（SAE），并开发了一个自适应权重混合集成（AWBE），该集成可以动态更新模型贡献。所有模型均优于基线恒定发病率模型，将 8 周预测的均方根误差（RMSE）降低了 23%-29%，加权区间评分（WIS）降低了 25%-31%。SAE 模型与个体模型表现相似，而 AWBE 模型将 RMSE 降低了 52%，WIS 降低了 53%，在不同流行趋势（增长、平稳、下降）和冬季和夏季期间的预测中均优于个体模型。使用 COVID-19 后数据（2023-2024 年）作为另一个测试期，AWBE 模型仍将 RMSE 降低了 39%，WIS 降低了 45%。我们的框架有助于在集合预测中比较和基准测试模型，为在流感季节性不规则的地理区域综合多种模型进行疾病预测提供了更多证据。
