title: Using global maps to predict the risk of dengue in Europe.
abstract: This article attempts to quantify the risk to Europe of dengue, following the arrival and spread there of one of dengue's vector species Aedes (Stegomyia) albopictus. A global risk map for dengue is presented, based on a global database of the occurrence of this disease, derived from electronic literature searches. Remotely sensed satellite data (from NASA's MODIS series), interpolated meteorological data, predicted distribution maps of dengue's two main vector species, Aedes aegypti and Aedes albopictus, a digital elevation surface and human population density data were all used as potential predictor variables in a non-linear discriminant analysis modelling framework. One hundred bootstrap models were produced by randomly sub-sampling three different training sets for dengue fever, severe dengue (i.e. dengue haemorrhagic fever, DHF) and all-dengue, and output predictions were averaged to produce a single global risk map for each type of dengue. This paper concentrates on the all-dengue models. Key predictor variables were various thermal data layers, including both day- and night-time Land Surface Temperature, human population density, and a variety of rainfall variables. The relative importance of each may be shown visually using rainbow files and quantitatively using a ranking system. Vegetation Index variables (a common proxy for humidity or saturation deficit) were rarely chosen in the models. The kappa index of agreement indicated an excellent (dengue haemorrhagic fever, Cohen's kappa=0.79 ± 0.028, AUC=0.96 ± 0.007) or good fit of the top ten models in each series to the data (Cohen's kappa=0.73 ± 0.018, AUC=0.94 ± 0.007 for dengue fever and 0.74 ± 0.017, AUC=0.95 ± 0.005 for all dengue). The global risk map predicts widespread dengue risk in SE Asia and India, in Central America and parts of coastal South America, but in relatively few regions of Africa. In many cases these are less extensive predictions than those of other published dengue risk maps and arise because of the key importance of high human population density for the all-dengue risk maps produced here. Three published dengue risk maps are compared using the Fleiss kappa index, and are shown to have only fair agreement globally (Fleiss kappa=0.377). Regionally the maps show greater (but still only moderate) agreement in SE Asia (Fleiss kappa=0.566), fair agreement in the Americas (Fleiss kappa=0.325) and only slight agreement in Africa (Fleiss kappa=0.095). The global dengue risk maps show that very few areas of rural Europe are presently suitable for dengue, but several major cities appear to be at some degree of risk, probably due to a combination of thermal conditions and high human population density, the top two variables in many models. Mahalanobis distance images were produced of Europe and the southern United States showing the distance in environmental rather than geographical space of each site from any site where dengue currently occurs. Parts of Europe are quite similar in Mahalanobis distance terms to parts of the southern United States, where dengue occurred in the recent past and which remain environmentally suitable for it. High standards of living rather than a changed environmental suitability keep dengue out of the USA. The threat of dengue to Europe at present is considered to be low but sufficiently uncertain to warrant monitoring in those areas of greatest predicted environmental suitability, especially in northern Italy and parts of Austria, Slovenia and Croatia, Bosnia and Herzegovina, Serbia and Montenegro, Albania, Greece, south-eastern France, Germany and Switzerland, and in smaller regions elsewhere.
authors: University of Oxford, Department of Zoology, South Parks Road, Oxford OX1 3PS, United Kingdom. Electronic address: david.rogers@zoo.ox.ac.uk.
authors: David J
authors: DJ
authors: Rogers
authors: Jonathan E
authors: JE
authors: Suk
authors: Jan C
authors: JC
authors: Semenza
mesh_terms: D000330:Aedes / Q000254:growth & development*; D000818:Animals; D003198:Computer Simulation; D003715:Dengue / Q000453:epidemiology* / Q000635:transmission*; D005060:Europe / Q000453:epidemiology; D014943:Global Health; D006801:Humans; D007303:Insect Vectors / Q000254:growth & development*; D011156:Population Density; D018570:Risk Assessment; D013696:Temperature
keywords: Dengue; Discriminant analysis; Fleiss kappa; Mahalanobis distance; Risk maps; Satellite data