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

Relationship between maximal temperatures and dengue outbreaks in Noumea.

<p>Averages and 95% confidence intervals (IC95%) of max Temp (<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001470#pntd-0001470-g004" target="_blank">Figure 4a</a>) and NOD_max Temp_32 (<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001470#pntd-0001470-g004" target="_blank">Figure 4b</a>) calculated monthly during epidemic and non epidemic years were compared from August (year <i>y</i>-1) to July (year <i>y</i>). The peak of max Temp preceded the epidemic peak of dengue with a lag of 1–2 months. The number of days with max Temp exceeding 32°C during the first quarter of the year was significantly higher during epidemic years than during non epidemic years, especially in February (NOD_max Temp_32_February = 7.25 versus 2 days, respectively).</p

SVM explicative model of dengue outbreaks in Noumea (leave-one-out cross validation).

<p>The model estimates the probability of dengue outbreak occurrence (red bars) each year according to the number of days with maximal temperature exceeding 32°C during the first quarter of the year (NOD_max Temp_32_JFM), and the number of days with maximal relative humidity exceeding 95% during January (NOD_max RH_95_January). Results obtained in leave-one-out cross validation are presented in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001470#pntd-0001470-g005" target="_blank">Figure 5a</a>. The black line indicates the annual dengue incidence rate, and black diamonds indicate epidemic years according to the median method. The ROC curve (<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001470#pntd-0001470-g005" target="_blank">Figure 5b</a>) indicates the rates of true and false positives for different detection thresholds. For example, for a probability of dengue outbreak above 65% (0.65), 15 of 20 epidemic years are predicted correctly (true positive rate = 75%) with only one false alarm (false positive rate = 5%). The sensitivity of the model for this threshold is 75% (15 epidemic years predicted correctly/20 epidemic years), the specificity 95% (19 non epidemic years predicted correctly/20 non epidemic years), the positive predictive value 94% (15 epidemic years predicted correctly/16 epidemic years predicted by the model), and the negative predictive value 79% (19 non epidemic years predicted correctly/24 non epidemic years predicted by the model).</p