BackgroundDengue dynamics are driven by complex interactions between human-hosts, mosquito-vectors and viruses that are influenced by environmental and climatic factors. The objectives of this study were to analyze and model the relationships between climate, Aedes aegypti vectors and dengue outbreaks in Noumea (New Caledonia), and to provide an early warning system.Methodology/Principal FindingsEpidemiological and meteorological data were analyzed from 1971 to 2010 in Noumea. Entomological surveillance indices were available from March 2000 to December 2009. During epidemic years, the distribution of dengue cases was highly seasonal. The epidemic peak (March–April) lagged the warmest temperature by 1–2 months and was in phase with maximum precipitations, relative humidity and entomological indices. Significant inter-annual correlations were observed between the risk of outbreak and summertime temperature, precipitations or relative humidity but not ENSO. Climate-based multivariate non-linear models were developed to estimate the yearly risk of dengue outbreak in Noumea. The best explicative meteorological variables were the number of days with maximal temperature exceeding 32°C during January–February–March and the number of days with maximal relative humidity exceeding 95% during January. The best predictive variables were the maximal temperature in December and maximal relative humidity during October–November–December of the previous year. For a probability of dengue outbreak above 65% in leave-one-out cross validation, the explicative model predicted 94% of the epidemic years and 79% of the non epidemic years, and the predictive model 79% and 65%, respectively.Conclusions/SignificanceThe epidemic dynamics of dengue in Noumea were essentially driven by climate during the last forty years. Specific conditions based on maximal temperature and relative humidity thresholds were determinant in outbreaks occurrence. Their persistence was also crucial. An operational model that will enable health authorities to anticipate the outbreak risk was successfully developed. Similar models may be developed to improve dengue management in other countries.

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In the analysis and prediction of real-world systems, two of the key problems are nonstationarity (often in the form of switching between regimes), and overfitting (particularly serious for noisy processes). This article addresses these problems using gated experts, consisting of a (nonlinear) gating network, and several (also nonlinear) competing experts. Each expert learns to predict the conditional mean, and each expert adapts its width to match the noise level in its regime. The gating network learns to predict the probability of each expert, given the input. This article focuses on the case where the gating network bases its decision on information from the inputs. This can be contrasted to hidden Markov models where the decision is based on the previous state(s) (i.e. on the output of the gating network at the previous time step), as well as to averaging over several predictors. In contrast, gated experts soft-partition the input space, only learning to model their region. This article discusses the underlying statistical assumptions, derives the weight update rules, and compares the performance of gated experts to standard methods on three time series: (1) a computer-generated series, obtained by randomly switching between two nonlinear processes; (2) a time series from the Santa Fe Time Series Competition (the light intensity of a laser in chaotic state); and (3) the daily electricity demand of France, a real-world multivariate problem with structure on several time scales. The main results are: (1) the gating network correctly discovers the different regimes of the process; (2) the widths associated with each expert are important for the segmentation task (and they can be used to characterize the sub-processes); and (3) there is less overfitting compared to single networks (homogeneous multilayer perceptrons), since the experts learn to match their variances to the (local) noise levels. This can be viewed as matching the local complexity of the model to the local complexity of the data.

Background/ObjectivesUnderstanding 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.MethodsWe 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.ResultsThe 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.ConclusionIn 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.

Many authors use feedforward neural networks for modeling and forecasting time series. Most of these applications are mainly experimental, and it is often difficult to extract a general methodology from the published studies. In particular, the choice of architecture is a tricky problem. We try to combine the statistical techniques of linear and nonlinear time series with the connectionist approach. The asymptotical properties of the estimators lead us to propose a systematic methodology to determine which weights are nonsignificant and to eliminate them to simplify the architecture. This method (SSM or statistical stepwise method) is compared to other pruning techniques and is applied to some artificial series, to the famous Sunspots benchmark, and to daily electrical consumption data.

BackgroundDengue is a mosquito-borne virus that causes extensive morbidity and economic loss in many tropical and subtropical regions of the world. Often present in cities, dengue virus is rapidly spreading due to urbanization, climate change and increased human movements. Dengue cases are often heterogeneously distributed throughout cities, suggesting that small-scale determinants influence dengue urban transmission. A better understanding of these determinants is crucial to efficiently target prevention measures such as vector control and education. The aim of this study was to determine which socioeconomic and environmental determinants were associated with dengue incidence in an urban setting in the Pacific.MethodologyAn ecological study was performed using data summarized by neighborhood (i.e. the neighborhood is the unit of analysis) from two dengue epidemics (2008–2009 and 2012–2013) in the city of Nouméa, the capital of New Caledonia. Spatial patterns and hotspots of dengue transmission were assessed using global and local Moran’s I statistics. Multivariable negative binomial regression models were used to investigate the association between dengue incidence and various socioeconomic and environmental factors throughout the city.Principal findingsThe 2008–2009 epidemic was spatially structured, with clusters of high and low incidence neighborhoods. In 2012–2013, dengue incidence rates were more homogeneous throughout the city. In all models tested, higher dengue incidence rates were consistently associated with lower socioeconomic status (higher unemployment, lower revenue or higher percentage of population born in the Pacific, which are interrelated). A higher percentage of apartments was associated with lower dengue incidence rates during both epidemics in all models but one. A link between vegetation coverage and dengue incidence rates was also detected, but the link varied depending on the model used.ConclusionsThis study demonstrates a robust spatial association between dengue incidence rates and socioeconomic status across the different neighborhoods of the city of Nouméa. Our findings provide useful information to guide policy and help target dengue prevention efforts where they are needed most.

Tropical cyclones (TCs) are large‐scale disturbances that regularly impact tropical forests. Although long‐term impacts of TCs on forest structure have been proposed, a global test of the relationship between forest structure and TC frequency and intensity is lacking. We test on a pantropical scale whether TCs shape the structure of tropical and subtropical forests in the long term. We compiled forest structural features (stem density, basal area, mean canopy height and maximum tree size) for plants ≥10 cm in diameter at breast height from published forest inventory data (438 plots ≥0.1 ha, pooled into 250 1 × 1‐degree grid cells) located in dry and humid forests. We computed maps of cyclone frequency and energy released by cyclones per unit area (power dissipation index, PDI) using a high‐resolution historical database of TCs trajectories and intensities. We then tested the relationship between PDI and forest structural features using multivariate linear models, controlling for climate (mean annual temperature and water availability) and human disturbance (human foot print). Forests subject to frequent cyclones (at least one TCs per decade) and high PDI exhibited higher stem density and basal area, and lower canopy heights. However, the relationships between PDI and basal area or canopy height were partially masked by lower water availability and higher human foot print in tropical dry forests. Synthesis. Our results provide the first evidence that tropical cyclones have a long‐term impact on the structure of tropical and subtropical forests in a globally consistent way. The strong relationship between power dissipation index and stem density suggests that frequent and intense tropical cyclones reduce canopy cover through defoliation and tree mortality, encouraging higher regeneration and turnover of biomass. The projected increase in intensity and poleward extension of tropical cyclones due to anthropogenic climate change may therefore have important and lasting impacts on the structure and dynamics of forests in the future.

BackgroundHigh incidences of malignant mesothelioma (MM) have been observed in New Caledonia. Previous work has shown an association between MM and soil containing serpentinite.ObjectivesWe studied the spatial and temporal variation of MM and its association with environmental factors.MethodsWe investigated the 109 MM cases recorded in the Cancer Registry of New Caledonia between 1984 and 2008 and performed spatial, temporal, and space–time cluster analyses. We conducted an ecological analysis involving 100 tribes over a large area including those with the highest incidence rates. Associations with environmental factors were assessed using logistic and Poisson regression analyses.ResultsThe highest incidence was observed in the Houaïlou area with a world age-standardized rate of 128.7 per 100,000 person-years [95% confidence interval (CI), 70.41–137.84]. A significant spatial cluster grouped 18 tribes (31 observed cases vs. 8 expected cases; p = 0.001), but no significant temporal clusters were identified. The ecological analyses identified serpentinite on roads as the greatest environmental risk factor (odds ratio = 495.0; 95% CI, 46.2–4679.7; multivariate incidence rate ratio = 13.0; 95% CI, 10.2–16.6). The risk increased with serpentinite surface, proximity to serpentinite quarries and distance to the peridotite massif. The association with serpentines was stronger than with amphiboles. Living on a slope and close to dense vegetation appeared protective. The use of whitewash, previously suggested to be a risk factor, was not associated with MM incidence.ConclusionsPresence of serpentinite on roads is a major environmental risk factor for mesothelioma in New Caledonia.