The aim of this study is to develop a model that correctly identifies and quantifies the relationship between dengue and meteorological factors in Guangzhou, China. By cross-correlation analysis, meteorological variables and their lag effects were determined. According to the epidemic characteristics of dengue in Guangzhou, those statistically significant variables were modeled by a zero-inflated Poisson regression model. The number of dengue cases and minimum temperature at 1-month lag, along with average relative humidity at 0- to 1-month lag were all positively correlated with the prevalence of dengue fever, whereas wind velocity and temperature in the same month along with rainfall at 2 months’ lag showed negative association with dengue incidence. Minimum temperature at 1-month lag and wind velocity in the same month had a greater impact on the dengue epidemic than other variables in Guangzhou.
GublerDJ. Dengue and dengue hemorrhagic fever. Clin Microbiol Rev. 1998;11:480-496.
2.
LiangWHeJLuoH. Epidemiological analysis of dengue fever in Guangdong province, 2001-2006. South China J Prev Med. 2007;33:4-6.
3.
LuoHHeJ. Analysis on the epidemiologic features of dengue fever in Guangdong province, 1990-2000. Zhonghua Liu Xing Bing Xue Za Zhi. 2002;32:427-428.
4.
RaheelUFaheemMRiazMN. Dengue fever in the Indian subcontinent: an overview. J Infect Dev Ctries. 2011;5:239-547.
5.
ThaiKTAndersKL. The role of climate variability and change in the transmission dynamics and geographic distribution of dengue. Exp Biol Med (Maywood). 2011;236:944-954.
6.
KunoG. Review of the factors modulating dengue transmission. Epidemiol Rev. 1995;17:321-335.
7.
HuWClementsAWilliamsGTongS. Dengue fever and El Nino/Southern Oscillation in Queensland, Australia: a time series predictive model. Occup Environ Med. 2010;67:307-311.
8.
HalsteadSB. Dengue virus-mosquito interactions. Annu Rev Entomol. 2008;53:273-291.
9.
YangHMMacorisMLGalvaniKCAndrighettiMTWanderleyDM. Assessing the effects of temperature on the population of Aedes aegypti, the vector of dengue. Epidemiol Infect. 2009;137:1188-1202.
10.
PatzJAMartensWJFocksDAJettenTH. Dengue fever epidemic potential as projected by general circulation models of global climate change. Environ Health Perspect. 1998;106:147-153.
11.
WattsDMBurkeDSHarrisonBAWhitmireRENisalakA. Effect of temperature on the vector efficiency of Aedes aegypti for dengue 2 virus. Am J Trop Med Hyg. 1987;36:143-152.
12.
BangsMJLarasatiRPCorwinALWuryadiS. Climatic factors associated with epidemic dengue in Palembang, Indonesia: implications of short-term meteorological events on virus transmission. Southeast Asian J Trop Med Public Health. 2006;37:1103-1116.
13.
MorrisonACZielinski-GutierrezEScottTWRosenbergR. Defining challenges and proposing solutions for control of the virus vectorAedes aegypti. PLoS Med. 2008;5:e68.
LambertD. Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics. 1992;34:1-14.
16.
FamoyeFSinghK. Zero-inflated generalized Poisson regression model with an application to domestic violence data. J Data Sci. 2006;4:117-130.
17.
BöhningD. A note on a test for Poisson overdispersion. Biometrika. 1994;81:418-419.
18.
RidoutMHindeJDemetrioCG. A score test for testing a zero-inflated Poisson regression model against zero-inflated negative binomial alternatives. Biometrics. 2001;57:219-223.
19.
WangCYangWFanJWangFJiangBLiuQ. Spatial and temporal patterns of dengue in Guangdong province of China [published online May 5, 2013]. Asia Pac J Public Health. doi:10.1177/1010539513477681.
20.
ShroutPE. Measurement reliability and agreement in psychiatry. Stat Methods Med Res. 1998;7:301-317.
21.
Ver HoefJMBovengPL. Quasi-Poisson vs. negative binomial regression: how should we model overdispersed count data?Ecology. 2007;88:2766-2772.
22.
YangHMMacorisMLGalvaniKCAndrighettiMTWanderleyDM. Assessing the effects of temperature on dengue transmission. Epidemiol Infect. 2009;137:1179-1187.
23.
GithekoAKLindsaySWConfalonieriUEPatzJA. Climate change and vector-borne diseases: a regional analysis. Bull World Health Organ. 2000;78:1136-1147.
24.
GharbiMQuenelPGustaveJ. Time series analysis of dengue incidence in Guadeloupe, French West Indies: forecasting models using climate variables as predictors. BMC Infect Dis. 2011;11:166.
25.
FavierCDegallierNVilarinhos PdeTde Carvalho MdoSYoshizawaMAKnoxMB. Effects of climate and different management strategies on Aedes aegypti breeding sites: a longitudinal survey in Brasilia (DF, Brazil). Trop Med Int Health. 2006;11:1104-1118.
26.
AzilAHLongSARitchieSAWilliamsCR. The development of predictive tools for pre-emptive dengue vector control: a study of Aedes aegypti abundance and meteorological variables in North Queensland, Australia. Trop Med Int Health. 2010;15:1190-1197.
27.
ZhengNWangZZhangX. Study on seasonal trend, propagating condition and the influencing factors of Aedes albopictus in Fuzhou, China. Haixia Yu Fang Yi Xue Za Zhi. 2001;7:6-9.
28.
HoppMJFoleyJA. Worldwide fluctuations in dengue fever cases related to climate variability. Climate Res. 2003;25:85-94.
29.
DepradineCLovellE. Climatological variables and the incidence of dengue fever in Barbados. Int J Environ Health Res. 2004;14:429-441.
30.
YasuokaJLevinsR. Ecology of vector mosquitoes in Sri Lanka: suggestions for future mosquito control in rice ecosystems. Southeast Asian J Trop Med Public Health. 2007;38:646-657.
31.
BarreraRAmadorMMacKayAJ. Population dynamics of Aedes aegypti and dengue as influenced by weather and human behavior in San Juan, Puerto Rico. PLoS Negl Trop Dis. 2011;5:e1378.
32.
WuJYLunZRJamesAAChenXG. Dengue fever in mainland China. Am J Trop Med Hyg. 2010;83:664-671.
33.
LuoLYangZWangY. Analysis on characteristics of dengue fever epidemic in Guangzhou, 2006. South China J Prev Med. 2007;33:11-14.
