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.

S U M M A R YAn algorithm for computing multivalued maps for traveltime, amplitude or any other ray-related variable is presented. It is based on a wavefront construction method, where the ray field is decomposed into elementary cells defined by adjacent rays and wavefronts. A sampling criterion for ray density in the phase space is suggested. I t is demonstrated that this new criterion ensures uniform ray density over the entire ray field including caustics. The method is applied to complex models.

The Brazilian Amazon is a large territory, where different weather systems act, contributing to non-homogeneity of the rainfall seasonal distribution in the region. The aim of this study is to determine sub-regions of homogeneous precipitation in the Amazon, linking them to the main atmospheric systems that affect the rainfall in the region. For this, hierarchical cluster analysis was applied on a data set composed by 305 rain gauges. The results suggest that the Brazilian Amazon has six pluvial homogeneous regions.

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Extreme precipitation often persists for multiple days with variable duration but has usually been examined at fixed duration. Here we show that considering extreme persistent precipitation by complete event with variable duration, rather than a fixed temporal period, is a necessary metric to account for the complexity of changing precipitation. Observed global mean annual‐maximum precipitation is significantly stronger (49.5%) for persistent extremes than daily extremes. However, both globally observed and modeled rates of relative increases are lower for persistent extremes compared to daily extremes, especially for Southern Hemisphere and large regions in the 0‐45°N latitude band. Climate models also show significant differences in the magnitude and partly even the sign of local mean changes between daily and persistent extremes in global warming projections. Changes in extreme precipitation therefore are more complex than previously reported, and extreme precipitation events with varying duration should be taken into account for future climate change assessments.