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 | |
|---|---|---|---|---|---|---|
| Â | Â | Risk factors | Disease/vector | Â | Â | Â |
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 |
-GIS | ||||||
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 | ||||||
-GIS | ||||||
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 | ||||||
-GIS | ||||||
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 |
-Auto-correlation | ||||||
-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 |