Spatial heterogeneity of dengue annual incidence rates in New Caledonia.

<p>Map of annual incidence rates per commune averaged over epidemic years of the 1995–2012 period (years 1995, 1996, 1998, 2003, 2004, 2008, 2009).</p

Results of the best multivariable model of the spatial structure of dengue incidence rates.

<p><b>A:</b> 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>B:</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.</p

Univariable and multivariable modelling of dengue average (across epidemic years) annual incidence rates: variable selection according to the RMSE of the SVM models

<p>* Variables included as explanatory variables for modelling dengue average (across epidemic years) annual incidence rates</p><p>** Root mean square error of each model, in number of cases /10,000 people / year. Models are classified first by the number of explanatory variables used, then by increasing RMSE.</p><p>Models highlighted in bold perform better than the best univariable model</p><p>Univariable and multivariable modelling of dengue average (across epidemic years) annual incidence rates: variable selection according to the RMSE of the SVM models</p

Principal component analysis over the set of climatic variables (A) and socio-economic variables (B).

<p>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.</p

Correlation between dengue incidence rates and socio-economic or climate variables

<p>* Pearson correlation coefficient (Rho) with dengue mean (across epidemic years) annual incidence rates and associated p-value. Variables are sorted by category (socio-economic or climate) and by decreasing order of their absolute Pearson correlation coefficient. Variables selected for the multivariable modelling are in bold</p><p>** Spc = Socio-professional category</p><p>Correlation between dengue incidence rates and socio-economic or climate variables</p

General map of New Caledonia.

<p>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).</p

Projections of temperature increase and predicted average annual incidence rates during epidemics for three time periods in the future.

<p>* Average of the mean temperature increase predicted by 6 coupled ocean-atmosphere models (see <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004211#sec006" target="_blank">Methods</a>)</p><p>** Calculated across the different GCM projections (see <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004211#pntd.0004211.s003" target="_blank">S3 Fig</a> for a representation of inter-model variability)</p><p>Projections of temperature increase and predicted average annual incidence rates during epidemics for three time periods in the future.</p

Maps of observed and predicted average annual incidence rates.

<p><b>A:</b> map of observed dengue annual incidence rates. <b>B and C:</b> 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). <b>D and E:</b> 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.</p

Epidemiology of dengue fever and evolution of annual mean temperature in Noumea-New Caledonia (1971–2010).

<p>The predominant circulating serotype (DENV-1, DENV-2, DENV-3 or DENV-4) is indicated in black characters. When other serotypes were detected, they are indicated in little grey characters. Annual dengue incidence rates observed in Noumea over the 1995–2010 period are highly correlated with dengue incidence rates observed in New Caledonia (Spearman coefficient <i>rho</i> = 0.99, <i>p</i>-value = 1*10⁻¹⁴). Annual dengue incidence rates in Noumea (1971–1994) were estimated (green dotted line with circles) on the basis of the relationship between incidence rates observed in New Caledonia (grey line) and those observed in Noumea (blue dotted line with crosses) using a linear model. During the 1971–2010 period, dengue incidence rates and annual mean temperatures (from January to December) were significantly correlated in Noumea (Spearman's coefficient <i>rho</i> = 0.426, <i>p</i>-value = 0.007). An increasing trend of dengue outbreaks amplitude and annual mean temperatures were observed during this 40-year study period.</p

Scatter plots of epidemic and non epidemic years with regards to NOD_max Temp_32_JFM and NOD_max RH_95_January.

<p>Each year, the number of days with maximal temperature exceeding 32°C during January–February–March (NOD_max Temp_32_JFM) and the number of days with maximal relative humidity exceeding 95% during January (NOD_max RH_95_January) were calculated. Two methods denoted “tercile method” and “median method” were used to separate the years on the basis of annual dengue incidence rates in Noumea (see <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001470#s2" target="_blank">Methods</a>). On the left panel, epidemic years (dengue incidence rate in the upper tercile, i.e. >19.48 cases/10,000 inhabitants/year) and non epidemic years (dengue incidence rate in the lower tercile, i.e. <4.13 cases/10,000 inhabitants/year) are presented. The distribution of crosses (epidemic years) and circles (non epidemic years) permits the identification of three groups (A, B, C). All non epidemic years belonged to group A whereas all epidemic years, except 1973 and 2003, belonged to either group B or group C suggesting that dengue outbreaks can occur in distinct climatic conditions. On the right panel, epidemic years (dengue incidence rate greater than the median, i.e. 7.65 cases/10,000 inhabitants/year) and non epidemic years (dengue incidence rate lower than the median) are presented with the advantage of a whole set of data being usable for modelling. Years that were not considered with the tercile method (dengue incidence rate in the middle tercile) are coloured in red. Further epidemic (red crosses) and non epidemic years (red circles) are considered with the median method, and similar groups (A, B, C) were identified. With the median method, three epidemic years (1978, 1979 and 1985) and one non epidemic year (2002) were incorrectly classified. These four years were characterized by annual dengue incidence rates closed to the median.</p