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Table 3 Characteristics of studies on the association between climatic variables and dengue transmission

From: Climate change and mosquito-borne diseases in China: a review

Study & Language Study area & period Data Collection Statistical methods Main findings Comments
   Risk factors Disease/vector    
Wu et al. (2011) English [47] Liaoning, Hebei, Shanxi, Shaanxi, Sichuan, and Gansu Province 1961-1990 Annual temperature and precipitation, the monthly temperature in January Distribution data of Aedes albopictus -CLIMEX model -Aedes albopictus have extended their geographic range to areas, which experienced the annual mean temperature below 11°C and the January mean temperature below -5°Cand this may be due to summer expansion -Risk maps of the potential distribution of Aedes albopictus in China were developed -No disease variables included
Lai et al. (2011) English [48] Kaohsiung City, Taiwan 2002-2007 Daily air temperature, amount of rainfall, relative humidity, sea surface temperature(SST) and weather patterns of typhoons Daily number of hospital admissions for dengue fever The incidence of dengue fever, Breteau Index -Cross-correlation -Hospital admissions for dengue in 2002 and 2005 were correlated with climatic factors with different time lags, including precipitation, temperature and the minimum relative humidity. -Both disease and vector factors were considered.
-Duncan's Multiple Range test -The impacts of SST and typhoons were discussed.
-Two case studies of dengue events were included.
-Spatial auto-correlation analysis
-Warm sea surface temperature and weather pattern of typhoons were major contributor to outbreaks of dengue
Chen et al. (2010) English [49] Taipei and Kaohsiung, Taiwan 2001-2008 Weekly minimum, mean, and maximum temperatures, relative humidity and rainfall Weekly dengue incidence Breteau Index -Poisson regression analysis -Weak positive relationships between dengue incidence and temperature variables in Taipei were found, whereas in Kaohsiung, all climatic factors were negatively correlated with dengue incidence -Both disease and vector factors were considered.
-Weekly indicators were used
-Spearman correlation
-Climatic factors with 3-month lag, and 1-month lag of percentage BI level >2 were the significant predictors of dengue incidence in Kaohsiung
Shang et al. (2010) English [50] Southern Taiwan (Tainan, Kaohsiung and Pingtung) 1998-2007 Daily mean temperature, maximum temperature, minimum temperature, relative humidity, wind speed, sunshine accumulation hours, sunshine rate, sunshine total flux and accumulative rainfall, accumulative rainy hours. Indigenous dengue cases Imported dengue cases -Logistic regression -An increase in imported case favors the occurrence of indigenous dengue when warmer and drier weather conditions are present -Simultaneously identify the relationship between indigenous and imported dengue cases in the context of meteorological factors
-Poisson regression
-Various climatic data were considered.
Lu et al. (2009) English [51] Guangzhou City, Guangdong Province 2001-2006 Monthly minimum temperature, maximum temperature, total rainfall, minimum relative humidity,wind velocity Monthly dengue fever cases and incidences -Spearman correlation -Dengue incidence was positively associated with minimum temperature and negatively with wind velocity. -A relative short 5-years study period.
-Other environmental and host factors were ignored.
-Poisson regression
Hsieh et al. (2009) English [52] Taiwan 2007 Typhoons, weekly temperature and total precipitation Weekly dengue incidence Initial reproduction numbers for the multi-wave outbreaks -Correlation analysis -A two-wave outbreaks with multiple turning points in 2007 were appeared to be led by the drastic drop in temperature and unusually large rainfall caused by the two consecutive typhoons. -The important role of climatological events in dengue outbreaks was evaluated.
-Multi-phase Richards model
Yang et al. (2009) English [53] Cixi area, Zhejiang Province (July-October, 2004) Daily average temperature, rainfall, relative humidity Case counts -Descriptive analysis -No relationship between the incidence of dengue and meteorological factors was observed during the outbreak in 2007 -A short 6-months study period.
- No statistical methods
Wu et al. (2009) English [54] Taiwan 1998-2002 Monthly temperature and rainfall Urbanization level Monthly incidence BI -Principle components analysis -Numbers of months with average temperature higher than 18°C and high degree of urbanization were identified as significant indicators for dengue fever infections -Both climatic variables and socioeconomic factors were considered.
-Logistic regression
Wu et al. (2007) English [55] Kaohsiung city, Taiwan 1998-2003 Monthly average temperature, maximum temperature, minimum temperature, relative humidity, and amount of rainfall Monthly incidence Vector density -Cross-correlation -Increased incidence of dengue fever was associated with decreased temperature and relative humidity. -Vector density was analyzed with dengue incidence Only one city was conducted
-Vector density did not found to be a good contributor of disease occurrences.
-ARIMA models
Lu et al. 2010 Chinese [56] The P.R. China 1970–2000 Guangzhou City and Fujian Province and Ningbo City 2004-2006 Weekly average temperature, maximum temperature, minimum temperature, relative humidity, rainfall and duration of sunshine Case counts -Correlation analysis -GIS -DF outbreaks were significantly correlated with climatic variables with 8–10 weeks lags. -A risk map of DF outbreaks for China with suitable weather conditions was developed
Yu et al. (2005) Chinese [57] Hainan Province (before1986, 1986–2001) Monthly temperature of January Predicted temperature of winter in 2020, 2030 and 2050 Infectious life span of infected mosquito -Descriptive analysis -Based on assumptions that temperatures in winter will increase by 1°C and 2°C in 2030 and 2050 respectively, half of or more areas in Hainan Province may be potentially favorable for dengue transmission all the year around by 2030 and 2050. -Long-term temperature data were collected
-GIS -Only considered the temperature
-Calculation of infectious life span of mosquito in different time periods -No disease data analysed
Chen et al. (2003) Chinese [58] Nine cities of Guangdong Province (Dec 2000- Nov 2001) Monthly mean temperature, relative humidity, rainfall and rainy days Case counts Breteau index -Descriptive analysis -The dengue fever intensity was highly related to increased temperature (>26°C), rainfall and consecutive rainy days (>10 days). -Study period was short -No statistical methods
Yi et al. (2003) Chinese [59] Chaozhou City, Guangdong Province 1995-2001 Monthly mean temperature, maximum temperature, minimum temperature, relative humidity, rainfall, rainy days, duration of sunshine Case counts Breteau index -Pearson correlation -Aedes density was positively correlated with temperature, rainfall, number of rainy days, duration of sunshine and negatively linked to relative humidity.
-Minimum temperature, rainfall and relative humidity are good predictors of Adeds density and dengue transmission.
-Various meteorological variables were used -Lag times of climatic factors were not analysed
-Both climatic variables and vector factors considered.
-Stepwise regression
-Logistic regression
Chen et al. (2002) Chinese [60] Hainan Province 1987-1996 Monthly temperature Infectious life span of infected mosquito -Descriptive analysis -If temperature increase by 1-2°C in winter, Hainan Province will be suitable for dengue transmission all the year around in future due to prolonged infectious life of mosquito. -Only considered the role of temperature
-No statistical methods
-Calculation of infectious life span of mosquito under different temperature
Zheng et al. (2001) Chinese [61] Fuzhou City, Fujian Province (2000–2001) Monthly mean temperature, relative humidity, rainfall Larva Density, House Index, Container Index, Breteau index, case counts -Descriptive analysis -The temperature and rainfall played a considerable role in vector density and dengue transmission, whereas relative humidity showed a little relationship. -Various mosquito density index used. Study period is relative short