Ensembles of leading European global coupled climate models show impressive reliability for seasonal climate prediction-including useful output for probabilistic prediction of malaria incidence and crop yield.

SummaryThe need to define the geographical distribution of Phlebotomus orientalis results from its importance as the dominant vector of kala azar (visceral leishmaniasis) in Sudan. Recent epidemics of this disease in southern and eastern Sudan caused an estimated 100 000 deaths and have renewed the impetus for defining the ecological boundaries of the vector. This information is an essential prerequisite to the production of a risk map for kala azar. This study uses data on the presence and absence of P. orientalis from 44 collecting sites across the central belt of Sudan. A logistic regression model was used to estimate the probability of the presence of P. orientalis at each collecting site as a function of climatic and environmental variables (rainfall; temperature; altitude; soil type and the satellite-derived environmental proxies -Normalized Difference Vegetation Index and Land Surface Temperature). The logistic regression model indicates mean annual maximum daily temperature and soil type as the most important ecological determinants of P. orientalis distribution. An initial risk map was created in a raster-based geographical information system which delineates the area where P. orientalis may occur. This map was then refined using a mask layer indicating the known rainfallbased boundaries of the distribution of Acacia-Balanites woodland -a woodland type known to be associated with the distribution of this vector. The predictive performance of the risk map is discussed.

No abstract

No abstract

Abstract. Over the last decade, remote sensing technologies and geographical information systems have moved from the research arena into the hands of vector control specialists. This review explains remote sensing approaches and spatial information technologies used for investigations of arthropod pests and vectors of diseases affecting humans and livestock. Relevant applications are summarized with examples of studies on African horse sickness vector Culicoides midges (Diptera: Ceratopogonidae), malaria vector Anopheles and arbovirus vector culicine mosquitoes (Diptera: Culicidae), leishmaniasis vector Phlebotomus sand¯ies (Diptera: Psychodidae), trypanosomiasis vector tsetse (Diptera: Glossinidae), loaiasis vector Chrysops (Diptera: Tabanidae), Lyme disease vector Ixodes and other ticks (Acari: Ixodidae). Methods and their uses are tabulated and discussed with recommendations for ef®ciency, caution and progress in this burgeoning ®eld.