Yuan Haokun, Lau Eric H Y, Cowling Benjamin J, Yang Wan
Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA.
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 Special Administrative Region, People's Republic of China.
J R Soc Interface. 2025 Jan;22(222):20240649. doi: 10.1098/rsif.2024.0649. Epub 2025 Jan 15.
Influenza forecasts could aid public health response as shown for temperate regions, but such efforts are more challenging in the tropics and subtropics due to more irregular influenza activities. Here, we built six forecast approaches for influenza in the (sub)tropics, with six model forms designed to model seasonal infection risk (i.e. seasonality) based on the dependence of virus survival on climate conditions and to flexibly account for immunity waning. We ran the models jointly with the ensemble adjustment Kalman filter to generate retrospective forecasts of influenza incidence in subtropical Hong Kong from January 1999 to December 2019 including the 2009 A(H1N1)pdm09 pandemic. In addition to short-term targets (one to four weeks ahead predictions), we also tested mid-range (one to three months) and long-range (four to six months) forecasts, which could be valuable for long-term planning. The largest improvement came from the inclusion of climate-modulated seasonality modelling, particularly for the mid- and long-range forecasts. The best-performing approach included a seasonal-trend-based climate modulation and assumed mixed immunity waning; the forecast accuracies, including peak week and intensity, were comparable to that reported for temperate regions including the USA. These findings demonstrate that incorporating mechanisms of climate modulation on influenza transmission can substantially improve forecast performance in the (sub)tropics.
流感预测有助于公共卫生应对，正如在温带地区所显示的那样，但由于热带和亚热带地区的流感活动更为不规则，此类工作面临更大挑战。在此，我们构建了六种针对（亚）热带地区流感的预测方法，设计了六种模型形式，旨在根据病毒存活对气候条件的依赖性来模拟季节性感染风险（即季节性），并灵活考虑免疫力衰退。我们将这些模型与集合调整卡尔曼滤波器联合运行，以生成1999年1月至2019年12月包括2009年甲型H1N1流感大流行在内的香港亚热带地区流感发病率的回顾性预测。除了短期目标（提前一至四周的预测），我们还测试了中期（一至三个月）和长期（四至六个月）预测，这对于长期规划可能很有价值。最大的改进来自纳入气候调节的季节性建模，特别是对于中期和长期预测。表现最佳的方法包括基于季节趋势的气候调节，并假设免疫力呈混合式衰退；其预测准确性，包括峰值周和强度，与包括美国在内的温带地区报告的结果相当。这些发现表明，纳入气候对流感传播调节机制可以显著提高（亚）热带地区的预测性能。
