Fig 1.
The map shows the location of towns (white dots), tribes (black dots), and weather stations registering temperature (red crosses) and rainfall (blue crosses) in New Caledonia. The background colour represents the digital elevation model (altitude).
Fig 2.
Maps of the 5 explanatory variables selected for modelling dengue incidence rates.
A: average mean temperature; B: average daily rainfall; C: average daily rainfall during the wettest quarter; D: percentage of unemployed people; E: average number of people per premise.
Table 1.
Correlation between dengue incidence rates and socio-economic or climate variables
Fig 3.
Spatial heterogeneity of dengue annual incidence rates in New Caledonia.
Map of annual incidence rates per commune averaged over epidemic years of the 1995–2012 period (years 1995, 1996, 1998, 2003, 2004, 2008, 2009).
Fig 4.
Principal component analysis over the set of climatic variables (A) and socio-economic variables (B).
The figure shows the correlation circles of PCA performed on the variables most spatially correlated with dengue average (across epidemic years) annual incidence rates (see methods/multivariable modelling of present dengue incidence rates/spatial autocorrelation of the response variable). Pearson correlation coefficients between variables can be approximated by the angle between the corresponding arrows: 1 for a 0° angle, 0 for a 90° angle, and -1 for a 180° angle.
Table 2.
Univariable and multivariable modelling of dengue average (across epidemic years) annual incidence rates: variable selection according to the RMSE of the SVM models
Fig 5.
Results of the best multivariable model of the spatial structure of dengue incidence rates.
A: Predicted mean (across epidemic years) annual incidence rates as a function of the two best explanatory variables (mean temperature and mean number of people per premise). The axes represent the value of the two best explanatory variables. Predicted average annual incidence rates are represented by the colour (blue for low incidence rates to orange for high incidence rates) and by the contour lines giving incidence rates in number of cases per 10,000 people per year. Each commune that has been used to build the model is placed on the graph according to the observed value of the two explanatory variables in the commune. Its position on the graph hence provides the average (across epidemic years) annual incidence rate in the commune as predicted by the model. For each commune, the coloured dot represents the difference between the predicted and the observed incidence rate (model error). B: Scatter plot of the predicted and observed average (across epidemic years) annual incidence rates for each of the 28 communes. The RMSE of this model is 45 cases per 10,000 per year.
Fig 6.
Maps of observed and predicted average annual incidence rates.
A: map of observed dengue annual incidence rates. B and C: maps of dengue annual incidence rates predicted by the SVM model (B) and the linear model (C) based on the mean temperature and the mean number of people per premise (over epidemic years of the study period). D and E: Trends of dengue spatial distribution under global warming. Average annual incidence rates during epidemics as projected over the 2080–2099 period under the RCP 4.5 (D) and the RCP 8.5 (E) scenarios.
Table 3.
Projections of temperature increase and predicted average annual incidence rates during epidemics for three time periods in the future.