Yin Yi, Lai Miao, Zhou Sijia, Chen Ziying, Jiang Xin, Wang Liping, Li Zhongjie, Peng Zhihang
School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
Division of Infectious Disease/Key Laboratory of Surveillance and Early Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China.
Infect Dis Model. 2023 Jul 8;8(3):822-831. doi: 10.1016/j.idm.2023.07.005. eCollection 2023 Sep.
Evidence is inefficient about how meteorological factors influence the trends of influenza transmission in different regions of China.
We estimated the time-varying reproduction number () of influenza and explored the impact of temperature and relative humidity on using generalized additive quasi-Poisson regression models combined with the distribution lag non-linear model (DLNM). The effect of temperature and humidity interaction on of influenza was explored. The multiple random-meta analysis was used to evaluate region-specific association. The excess risk (ER) index was defined to investigate the correlation between and each meteorological factor with the modification of seasonal and regional characteristics.
Low temperature and low relative humidity contributed to influenza epidemics on the national level, while shapes of merged cumulative effect plots were different across regions. Compared to that of median temperature, the merged RR (95%CI) of low temperature in northern and southern regions were 1.40(1.24,1.45) and 1.20 (1.14,1.27), respectively, while those of high temperature were 1.10(1.03,1.17) and 1.00 (0.95,1.04), respectively. There were negative interactions between temperature and relative humidity on national (SI = 0.59, 95%CI: 0.57-0.61), southern (SI = 0.49, 95%CI: 0.17-0.80), and northern regions (SI = 0.59, 95%CI: 0.56,0.62). In general, with the increase of the change of the two meteorological factors, the ER of also gradually increased.
Temperature and relative humidity have an effect on the influenza epidemics in China, and there is an interaction between the two meteorological factors, but the effect of each factor is heterogeneous among regions. Meteorological factors may be considered to predict the trend of influenza epidemic.
关于气象因素如何影响中国不同地区流感传播趋势的证据并不充分。
我们使用广义相加拟泊松回归模型结合分布滞后非线性模型(DLNM)估计流感的时间变化繁殖数(),并探讨温度和相对湿度对的影响。研究了温度和湿度相互作用对流感的影响。采用多重随机荟萃分析评估特定区域的关联。定义超额风险(ER)指数,以研究在考虑季节和区域特征的情况下与各气象因素之间的相关性。
在全国范围内,低温和低相对湿度会导致流感流行,而不同地区合并累积效应图的形状有所不同。与中位温度相比,北方和南方地区低温的合并RR(95%CI)分别为1.40(1.24,1.45)和1.20(1.14,1.27),而高温的合并RR分别为1.10(1.03,1.17)和1.00(0.95,1.04)。在全国(SI = 0.59,95%CI:0.57 - 0.61)、南方(SI = 0.49,95%CI:0.17 - 0.80)和北方地区(SI = 0.59,95%CI:0.56,0.62),温度和相对湿度之间存在负相互作用。总体而言,随着这两个气象因素变化的增加,的ER也逐渐增加。
温度和相对湿度对中国的流感流行有影响,这两个气象因素之间存在相互作用,但各因素的影响在不同地区存在异质性。可考虑气象因素来预测流感流行趋势。