Different responses of influenza epidemic to weather factors among Shanghai, Hong Kong, and British Columbia.
Weather factors have long been considered as key sources for regional heterogeneity of influenza seasonal patterns. As influenza peaks coincide with both high and low temperature in subtropical cities, weather factors may nonlinearly or interactively affect influenza activity. This study aims to assess the nonlinear and interactive effects of weather factors with influenza activity and compare the responses of influenza epidemic to weather factors in two subtropical regions of southern China (Shanghai and Hong Kong) and one temperate province of Canada (British Columbia). Weekly data on influenza activity and weather factors (i.e., mean temperature and relative humidity (RH)) were obtained from pertinent government departments for the three regions. Absolute humidity (AH) was measured by vapor pressure (VP), which could be converted from temperature and RH. Generalized additive models were used to assess the exposure-response relationship between weather factors and influenza virus activity. Interactions of weather factors were further assessed by bivariate response models and stratification analyses. The exposure-response curves of temperature and VP, but not RH, were consistent among three regions/cities. Bivariate response model revealed a significant interactive effect between temperature (or VP) and RH (P < 0.05). Influenza peaked at low temperature or high temperature with high RH. Temperature and VP are important weather factors in developing a universal model to explain seasonal outbreaks of influenza. However, further research is needed to assess the association between weather factors and influenza activity in a wider context of social and environmental conditions.
Department of Biostatistics, School of Public Health and Key Laboratory of Public Health Safety, Fudan University, Shanghai, People's Republic of China.|Shanghai Key Laboratory of Meteorology and Health, Shanghai, People's Republic of China.
Xi-Ling
XL
Wang
School of Nursing, The Hong Kong Polytechnic University, Hong Kong, SAR, People's Republic of China.
Lin
L
Yang
Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, SAR, People's Republic of China.
Dai-Hai
DH
He
Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, SAR, People's Republic of China.
Alice Py
AP
Chiu
Department of Microbiology, The University of Hong Kong, Hong Kong, SAR, People's Republic of China.
Kwok-Hung
KH
Chan
School of Public Health, The University of Hong Kong, Hong Kong, SAR, People's Republic of China.
King-Pan
KP
Chan
National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, People's Republic of China.
Maigeng
M
Zhou
School of Public Health, The University of Hong Kong, Hong Kong, SAR, People's Republic of China.
Chit-Ming
CM
Wong
Center for Public Health Surveillance and Information Service, Chinese Center for Disease Control and Prevention, Beijing, China. guoqing@chinacdc.cn.
Qing
Q
Guo
Department of Biostatistics, School of Public Health and Key Laboratory of Public Health Safety, Fudan University, Shanghai, People's Republic of China. w2.hu@qut.edu.au.|School of Public Health and Social Work, The Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, Brisbane, Queensland, 4059, Australia. w2.hu@qut.edu.au.
Wenbiao
W
Hu
D001955:British Columbia / Q000453:epidemiology; D002681:China / Q000453:epidemiology; D058872:Epidemics; D006723:Hong Kong / Q000453:epidemiology; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology*; D008962:Models, Theoretical; D014887:Weather
Generalized additive model; Influenza; Interaction; Meteorology; Seasonality