Socio-economic and Climate Factors Associated with Dengue Fever Spatial Heterogeneity: A Worked Example in New Caledonia

International audienceUnderstanding the factors underlying the spatio-temporal distribution of infectious diseases provides useful information regarding their prevention and control. Dengue fever spatio-temporal patterns result from complex interactions between the virus, the host, and the vector. These interactions can be influenced by environmental conditions. Our objectives were to analyse dengue fever spatial distribution over New Caledonia during epidemic years, to identify some of the main underlying factors, and to predict the spatial evolution of dengue fever under changing climatic conditions, at the 2100 horizon. We used principal component analysis and support vector machines to analyse and model the influence of climate and socio-economic variables on the mean spatial distribution of 24,272 dengue cases reported from 1995 to 2012 in thirty-three communes of New Caledonia. We then modelled and estimated the future evolution of dengue incidence rates using a regional downscaling of future climate projections. The spatial distribution of dengue fever cases is highly heterogeneous. The variables most associated with this observed heterogeneity are the mean temperature, the mean number of people per premise, and the mean percentage of unemployed people, a variable highly correlated with people's way of life. Rainfall does not seem to play an important role in the spatial distribution of dengue cases during epidemics. By the end of the 21st century, if temperature increases by approximately 3°C, mean incidence rates during epidemics could double. In New Caledonia, a subtropical insular environment, both temperature and socio-economic conditions are influencing the spatial spread of dengue fever. Extension of this study to other countries worldwide should improve the knowledge about climate influence on dengue burden and about the complex interplay between different factors. This study presents a methodology that can be used as a step by step guide to model dengue spatial heterogeneity in other countries

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

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

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

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

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

BASSINS VERSANTSFonctionnement des petits bassins versants miniers Volume 1- Rapport de synthèse

Le programme « Fonctionnement des petits bassins versant miniers » a fait l’objet d’unerestitution publique le 2 Décembre 2014. Parallèlement à cette restitution, un rapportscientifique a été produit, regroupant l’ensemble des contributions des différentspartenaires du consortium scientifique. Le lecteur de la présente synthèse pourrautilement s’y référer pour obtenir les éléments d’information dont il pourrait avoir besoin pourcompléter ceux qui vont être présentés dans ce rapport, sous un format volontairementréduit.L’objectif du présent document est de présenter à l’attention prioritaire de la professionet des services techniques des collectivités, les résultats essentiels que l’on peutdégager des efforts de recherche réalisés dans le cadre du programme, les avancéesobtenues, mais aussi les difficultés rencontrées et les actions restant à produire pourconforter les acquis du programme. Le formalisme propre aux publications scientifiquessera donc volontairement réduit et la présentation ne se veut pas exhaustive desrésultats produits par le programme. Elle gomme volontairement la majeure partie desdéveloppements méthodologiques et les raisonnements scientifiques qui seront à rechercherdans le rapport détaillé.Le rapport de synthèse est articulé en deux volets principaux :• Le premier est consacré à la présentation générale du programme, de ses acteurs, deleurs rôles respectifs, des zones étudiées et du planning des études tel qu’il a étécontractualisé ;• Le second décrit les résultats essentiels regroupés sous quatre rubriques : l’hydrologieminière, les modélisations, le programme de transfert et de formation, et les autresrésultats.Un court chapitre de conclusion s’efforcera de faire le bilan du programme en précisant lesdifficultés rencontrées et les pistes conseillées pour tirer un maximum de profits des résultatsengrangés