Time series analysis of dengue fever and weather in Guangzhou, China
Liang Lua b, Hualiang Linc d e f, Linwei Tiand f✉, Weizhong Yangg h, Jimin Suni, Qiyong Liuc e j✉
- aNational Institute for Communicable Disease Control and Prevention, China CDC, Beijing, PR China. luliang28@gmail.com
- bNational Institute for Communicable Disease Control and Prevention, China CDC, Beijing, PR China
- cNational Institute for Communicable Disease Control and Prevention, China CDC, Beijing, P.R. China
- dStanley Ho Centre for Emerging Infectious Diseases, School of Public Health, Chinese University of Hong Kong, Hong Kong, SAR, P.R. China
- eNational Institute for Communicable Disease Control and Prevention, Beijing, P.R. China
- fStanley Ho Centre for Emerging Infectious Diseases, School of Public Health, Chinese University of Hong Kong, Hong Kong, P.R. China
- gChinese Center for Disease Control and Prevention, Beijing, P.R. China
- hChinese Center for Disease Control and Prevention, Beijing, P R China
- iZhejiang Provincial Center for Disease Control and Prevention, Hangzhou, P.R. China
- j
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Abstract
BACKGROUND: Monitoring and predicting dengue incidence facilitates early public health responses to minimize morbidity and mortality. Weather variables are potential predictors of dengue incidence. This study explored the impact of weather variability on the transmission of dengue fever in the subtropical city of Guangzhou, China. METHODS: Time series Poisson regression analysis was performed using data on monthly weather variables and monthly notified cases of dengue fever in Guangzhou, China for the period of 2001-2006. Estimates of the Poisson model parameters was implemented using the Generalized Estimating Equation (GEE) approach; the quasi-likelihood based information criterion (QICu) was used to select the most parsimonious model. RESULTS: Two best fitting models, with the smallest QICu values, are selected to characterize the relationship between monthly dengue incidence and weather variables. Minimum temperature and wind velocity are significant predictors of dengue incidence. Further inclusion of minimum humidity in the model provides a better fit. CONCLUSION: Minimum temperature and minimum humidity, at a lag of one month, are positively associated with dengue incidence in the subtropical city of Guangzhou, China. Wind velocity is inversely associated with dengue incidence of the same month. These findings should be considered in the prediction of future patterns of dengue transmission.