Meteorological Factors for Dengue Fever Control and Prevention in South China

Haogao Gu, Ross Ka-Kit Leung, Qinlong Jing, Wangjian Zhang, Zhicong Yang, Jiahai Lu, Yuantao Hao and Dingmei Zhang
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Haogao Gu: Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
Ross Ka-Kit Leung: Division of Public Health Laboratory Sciences, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, China
Qinlong Jing: Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
Wangjian Zhang: Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
Zhicong Yang: Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, China
Jiahai Lu: Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
Yuantao Hao: Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
Dingmei Zhang: Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China

IJERPH, 2016, vol. 13, issue 9, 1-12

Abstract: Dengue fever (DF) is endemic in Guangzhou and has been circulating for decades, causing significant economic loss. DF prevention mainly relies on mosquito control and change in lifestyle. However, alert fatigue may partially limit the success of these countermeasures. This study investigated the delayed effect of meteorological factors, as well as the relationships between five climatic variables and the risk for DF by boosted regression trees (BRT) over the period of 2005–2011, to determine the best timing and strategy for adapting such preventive measures. The most important meteorological factor was daily average temperature. We used BRT to investigate the lagged relationship between dengue clinical burden and climatic variables, with the 58 and 62 day lag models attaining the largest area under the curve. The climatic factors presented similar patterns between these two lag models, which can be used as references for DF prevention in the early stage. Our results facilitate the development of the Mosquito Breeding Risk Index for early warning systems. The availability of meteorological data and modeling methods enables the extension of the application to other vector-borne diseases endemic in tropical and subtropical countries.

Keywords: dengue fever; boosted regression trees; meteorological effects (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:13:y:2016:i:9:p:867-:d:77118

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