Background
The ecology of dengue and vector
Dengue and climate change
Quantitative modelling of dengue and climate
Methods
Search strategy
Selection criteria
Results
References | Study area (Period) | Dengue data | Covariate data | Spatial resolution | Analytical approaches | Key findings | Comments |
---|---|---|---|---|---|---|---|
Earnest et al. [32] | Singapore (2001–2008) | Weekly laboratory confirmed notified dengue cases | Weekly climate (mean/minimum/maximum temperature, mean rainfall, mean/minimum/maximum relative humidity, mean hours of sunshine and mean hours of cloud) data | Local meteorological station data | Poisson regression, Sinusoidal function | Temperature, relative humidity and SOI associated with dengue cases. | Temporal trends of dengue were noticeable. |
Descloux et al. [31] | Noumea (New Caledonia) (1971–2010) | Monthly confirmed cases of DF/ DHF | Monthly climate (temperature, precipitation, relative humidity, wind force, potential evapo-transpiration, hydric balance sheet) data and ENSO indices | Local meteorological station data | Non-linear models | Significant inter-annual correlations were observed between dengue outbreaks and summertime temperature, precipitation, relative humidity but not ENSO. | The epidemic dynamics of dengue were driven by climate. |
Chen et al. [30] | Taiwan (1994–2008) | Daily confirmed cases of notified DF | Daily climate (temperature, rainfall) data, socio-demographic factors | Local meteorological station data | GAM | Rainfall was correlated with dengue cases. Lag effects were observed. | A climatic change does have influence on dengue outbreaks. |
Hu et al. [43] | Australia (2002–2005) | Monthly confirmed cases of notified dengue | Monthly weather, SEIFA, pop (LGA) | Local meteorological station data | Bayesian CAR | Increase in dengue cases of 6% in association with a 1-mm increase in average monthly rainfall and a 1°C increase in average monthly maximum temperature, respectively was observed. | Socio-ecological factors appear to influence dengue. The drivers may differ for local and overseas cases. Spatial clustering of dengue cases was evident. |
Chowell [45] | Peru (1994–2008) | Annual confirmed cases | Time series of annual population size and density, altitude and climate data | Local meteorological station data | Wavelet time series | A significant difference in the timing of epidemics between jungle and coastal regions was observed. | The differences in the timing of dengue epidemics between jungle and coastal regions were significantly associated with the timing of the seasonal temperature cycle. |
Thai et al. [50] | Vietnam (1994–2009) | Monthly confirmed cases | Monthly climate (mean temperature, rainfall and relative humidity) data and ENSO indices | Local meteorological station data | Wavelet time series | ENSO indices and climate variables were significantly associated with dengue incidence. | Climate variability and ENSO impact dengue outbreaks. |
Colon-Gonzalez et al. [41] | Mexico (1985–2007) | Monthly confirmed cases | Monthly climate (minimum and maximum temperature and rainfall) and ENSO indices | Local meteorological station data | Linear regression, Phillips–Perron and Jarque–Bera test tests | Incidence was higher during El-Nino. Incidence was associated with El-Nino and temperature during cool and dry times. | Temperature was an important factor in the dengue incidence in Mexico. |
Pinto et al. [9] | Singapore (2000–2007) | Weekly confirmed notified DF cases | Weekly climate (maximum and minimum temperature, maximum and minimum relative humidity) data | Local meteorological station data | Poisson regression, Principal component anlaysis | For every 2–10 degrees of maximum and minimum temperature variation, an increase of cases of 22-184% and 26-230% respectively, was observed. | Temperature was the best predictor for the dengue increase in Singapore. |
Gharbi et al. [36] | French West Indies (2000–2007) | Weekly laboratory confirmed cases from hospitals or not | Weekly climate (cumulative rainfall, relative humidity, minimum, maximum and average temperature) data | Local meteorological station data | Time series (SARIMA), RMSE and Wilcoxon signed-ranks test | Temperature was significantly associated with dengue forecasting but not humidity. | Temperature improves dengue outbreaks better than humidity and rainfall. |
Hu et al. [42] | Australia (1993–2005) | Monthly confirmed cases of notified DF cases | Monthly SOI, rainfall and annual population | Local meteorological station data | Cross-correlations, Time series (SARIMA) | A decrease in the SOI was significantly associated with an increase in the dengue cases. | Climate variability is directly and/or indirectly associated with dengue. SOI based epidemic forecasting is possible. |
Johansson et al. [48] | Puerto Rico, Mexico, Thailand (1986–2006) | Monthly reported cases of DF/ DHF | Monthly climate (precipitation, minimum, maximum and mean average temperature) data and ENSO indices | Global climate surfaces (0.5 × 0.5°) local meteorological station data | Wavelet time series | Temperature, rainfall and dengue incidence were strongly associated in all three countries for the annual cycle. The associations with ENSO varied between countries in the multi-annual cycle. | The role of ENSO may be obscured by local climate heterogeneity, insufficient data, randomly coincident outbreaks, and other, potentially stronger, intrinsic factors regulating dengue transmission dynamics. |
Bambrick et al. [44] | Australia (1991–2007) | Annual incidence – notified cases of DF | Annual Temperature, vapour pressure and population | Local meteorological station data | Climate change scenarios | Geographic regions with climates that are favourable to dengue could expand to include large population centres in a number of currently dengue-free regions. | An eight-fold increase in the number of people living in dengue prone regions in Australia will occur unless greenhouses gases are reduced. |
Bulto et al. [46] | Cuba (1961–1990) | Dengue-specific parameters of DF/ DHF | Monthly climate (maximum and minimum temperature, precipitation, atmospheric pressure, vapour pressure, relative humidity, thermal oscillation and solar radiation) data | Local meteorological station data | Multivariate (Empiric orthogonal function) | Strong associations between climate anomalies and dengue | Climate variability has influence on dengue. |
Cazelles et al. [40] | Thailand (1983–1997) | Monthly confirmed cases of DHF | Monthly climate (temperature and rainfall) data and ENSO indices | Local meteorological station data | Wavelet time series | Strong association between dengue incidence and El-Nino events was observed. Temperature had greater influence on dengue than rainfall. | The association is non-stationary and have a major influence on the synchrony of dengue epidemics. |
Hales et al. [33] | Global (1975–1996) | Monthly reported cases of DF | Monthly climate (maximum, minimum and mean temperature, rainfall and vapour pressure) and population and projections (future climate and population) data | Region-specific and GCM projections | Logistic regression and IPCC scenarios | In 2085, under climate and population projections, 50-60% of the population would be at dengue risk. | There is a potential increase in the dengue risk areas under climate change scenarios, if the risk factors remain constant. |
Patz et al. [38] | Global (1931–1980) | Dengue-specific parameters | Monthly climate data | Site-specific GCM | GCM output to vectorial capacity | Among the three GCMs, the average projected temperature elevation was 1.16°C, expected by the year 2050. | Epidemic potential increased with a relatively small temperature rise, indicating that lower mosquitoes infestation values would be necessary to maintain or spread dengue in a vulnerable population. |