目标:在不同的时间,公共卫生面临各种挑战,干预措施的程度各不相同。气象因素对流感的影响和预测研究逐渐增多,然而,目前没有证据表明其研究结果是否受到不同时期的影响。本研究旨在提供有限的证据来揭示这一问题。
方法:将厦门市影响因素和流感的每日数据分为三个部分:总体期间(AB期),非COVID-19流行期(A阶段),和COVID-19流行期(B期)。采用广义加性模型(GAMs)分析影响因素与流感的关系。当气象因素的四分位数间隔(IQR)增加时,超额风险(ER)用于表示流感的百分比变化。采用双向长短记忆(Bi-LSTM)和随机森林(RF)相结合,通过前7天每日多因子值的多步骤滚动输入,对7天平均每日流感病例进行预测。
结果:在A和AB时段,气温低于22°C是流感的危险因素.然而,在B阶段,温度对它表现出U形效应。相对湿度在AB期比A期对流感有更显著的累积效应(峰值:累积14d,AB:ER=281.54,95%CI=245.47~321.37;A:ER=120.48,95%CI=100.37~142.60)。与其他年龄组相比,4-12岁的儿童受压力影响更大,降水,阳光,和日光,而年龄≥13岁的人群受湿度累积的影响更大。预测流感的准确性在A期最高,在B期最低。
结论:在不同阶段采取不同程度的干预措施,导致气象因素对流感的影响和对流感的预测存在显著差异。在呼吸道传染病的关联研究中,尤其是流感,和环境因素,建议排除有更多外部干预措施的时期,以减少对环境因素和流感相关研究的干扰,或完善模型以适应干预措施带来的变化。此外,RF-Bi-LSTM模型对流感具有良好的预测性能。
OBJECTIVE: At different times, public health faces various challenges and the degree of intervention measures varies. The research on the impact and prediction of meteorology factors on influenza is increasing gradually, however, there is currently no evidence on whether its research results are affected by different periods. This study aims to provide limited evidence to reveal this issue.
METHODS: Daily data on influencing factors and influenza in Xiamen were divided into three parts: overall period (phase AB), non-COVID-19 epidemic period (phase A), and COVID-19 epidemic period (phase B). The association between influencing factors and influenza was analysed using generalized additive models (GAMs). The excess risk (ER) was used to represent the percentage change in influenza as the interquartile interval (IQR) of meteorology factors increases. The 7-day average daily influenza cases were predicted using the combination of bi-directional long short memory (Bi-LSTM) and random forest (RF) through multi-step rolling input of the daily multifactor values of the previous 7-day.
RESULTS: In periods A and AB, air temperature below 22 °C was a risk factor for influenza. However, in phase B, temperature showed a U-shaped effect on it. Relative humidity had a more significant cumulative effect on influenza in phase AB than in phase A (peak: accumulate 14d, AB: ER = 281.54, 95% CI = 245.47 ~ 321.37; A: ER = 120.48, 95% CI = 100.37 ~ 142.60). Compared to other age groups, children aged 4-12 were more affected by pressure, precipitation, sunshine, and day light, while those aged ≥ 13 were more affected by the accumulation of humidity over multiple days. The accuracy of predicting influenza was highest in phase A and lowest in phase B.
CONCLUSIONS: The varying degrees of intervention measures adopted during different phases led to significant differences in the impact of meteorology factors on influenza and in the influenza prediction. In association studies of respiratory infectious diseases, especially influenza, and environmental factors, it is advisable to exclude periods with more external interventions to reduce interference with environmental factors and influenza related research, or to refine the model to accommodate the alterations brought about by intervention measures. In addition, the RF-Bi-LSTM model has good predictive performance for influenza.