School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia.
Research Base of Key Laboratory of Surveillance and Early Warning of Infectious Disease, Pudong New Area Center for Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Shanghai, China.
Sci Total Environ. 2020 Jan 20;701:134607. doi: 10.1016/j.scitotenv.2019.134607. Epub 2019 Oct 28.
Most previous studies focused on the association between climate variables and seasonal influenza activity in tropical or temperate zones, little is known about the associations in different influenza types in subtropical China. The study aimed to explore the associations of multiple climate variables with influenza A (Flu-A) and B virus (Flu-B) transmissions in Shanghai, China. Weekly influenza virus and climate data (mean temperature (MeanT), diurnal temperature range (DTR), relative humidity (RH) and wind velocity (Wv)) were collected between June 2012 and December 2018. Generalized linear models (GLMs), distributed lag non-linear models (DLNMs) and regression tree models were developed to assess such associations. MeanT exerted the peaking risk of Flu-A at 1.4 °C (2-weeks' cumulative relative risk (RR): 14.88, 95% confidence interval (CI): 8.67-23.31) and 25.8 °C (RR: 12.21, 95%CI: 6.64-19.83), Flu-B had the peak at 1.4 °C (RR: 26.44, 95%CI: 11.52-51.86). The highest RR of Flu-A was 23.05 (95%CI: 5.12-88.45) at DTR of 15.8 °C, that of Flu-B was 38.25 (95%CI: 15.82-87.61) at 3.2 °C. RH of 51.5% had the highest RR of Flu-A (9.98, 95%CI: 4.03-26.28) and Flu-B (4.63, 95%CI: 1.95-11.27). Wv of 3.5 m/s exerted the peaking RR of Flu-A (7.48, 95%CI: 2.73-30.04) and Flu-B (7.87, 95%CI: 5.53-11.91). DTR ≥ 12 °C and MeanT <22 °C were the key drivers for Flu-A and Flu-B, separately. The study found complex non-linear relationships between climate variability and different influenza types in Shanghai. We suggest the careful use of meteorological variables in influenza prediction in subtropical regions, considering such complex associations, which may facilitate government and health authorities to better minimize the impacts of seasonal influenza.
大多数先前的研究都集中在气候变量与热带或温带地区季节性流感活动之间的关联上，而对于亚热带中国不同流感类型的关联知之甚少。本研究旨在探讨多种气候变量与中国上海甲型流感（Flu-A）和乙型流感（Flu-B）传播之间的关联。在 2012 年 6 月至 2018 年 12 月期间，每周收集流感病毒和气候数据（平均温度（MeanT）、日温差（DTR）、相对湿度（RH）和风速（Wv））。使用广义线性模型（GLMs）、分布滞后非线性模型（DLNMs）和回归树模型来评估这种关联。MeanT 对 Flu-A 的发病高峰出现在 1.4°C（2 周累积相对风险（RR）：14.88，95%置信区间（CI）：8.67-23.31）和 25.8°C（RR：12.21，95%CI：6.64-19.83），Flu-B 的发病高峰出现在 1.4°C（RR：26.44，95%CI：11.52-51.86）。Flu-A 的最高 RR 为 23.05（95%CI：5.12-88.45），DTR 为 15.8°C，Flu-B 的最高 RR 为 38.25（95%CI：15.82-87.61），DTR 为 3.2°C。相对湿度为 51.5%时，Flu-A 的 RR 最高（9.98，95%CI：4.03-26.28）和 Flu-B（4.63，95%CI：1.95-11.27）。风速为 3.5 m/s 时，Flu-A（7.48，95%CI：2.73-30.04）和 Flu-B（7.87，95%CI：5.53-11.91）的发病高峰 RR。DTR≥12°C 和 MeanT <22°C 是 Flu-A 和 Flu-B 的关键驱动因素。研究发现，上海不同流感类型与气候变量之间存在复杂的非线性关系。我们建议在亚热带地区流感预测中谨慎使用气象变量，考虑到这种复杂的关联，这可能有助于政府和卫生当局更好地将季节性流感的影响降至最低。
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