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This study aims to provide trypanosomiasis-affected countries with standardized datasets and methodologies for mapping the habitat of the tsetse fly (Glossina spp., the disease vector) by customizing and integrating state-of-the-art land cover maps on different spatial scales. Using a combination of inductive and deductive approaches, land cover and fly distribution maps are analysed in a geographic information system (GIS) to estimate the suitability of different land cover units for the three groups (subgenera) of Glossina. All land cover datasets used for and produced by the study comply with the Land Cover Classification System (LCCS). At the continental scale, a strong correlation between land cover and tsetse habitat is found for both the fusca and palpalis groups, whereas a weaker correlation found for the morsitans group may be indicative of less restrictive ecological requirements. At the regional and national levels, thematic aggregation of the multi-purpose Africover datasets yielded high-resolution, standardized land cover maps tailored for tsetse habitat for eight East African countries. The national maps provide remarkable spatial resolution, thematic detail and geographical coverage. They may be applied in subsequent phases of tsetse and trypanosomiasis control projects, including the planning of entomological surveys, actual tsetse control operations and planning for land use in reclaimed areas. The methodology and datasets discussed in the paper may have applications beyond the tsetse and trypanosomiasis issue and may be used with reference to other arthropod vectors, vector-borne and parasitic diseases.

During September–October 2010, an unprecedented outbreak of Rift Valley fever was reported in the northern Sahelian region of Mauritania after exceptionally heavy rainfall. Camels probably played a central role in the local amplification of the virus. We describe the main clinical signs (hemorrhagic fever, icterus, and nervous symptoms) observed during the outbreak.

A Rift Valley fever (RVF) outbreak occurred in Madagascar from January to May 2008. The objectives of this study were (1) to assess the current and past circulation of RVF virus (RVFV) in livestock in Madagascar and (2) to evaluate the extent and magnitude of the 2008 RVF outbreak in livestock. The results of a country-wide serosurvey conducted in August 2008 on small and large ruminants are reported here. The study included 3437 cattle and 989 small ruminants (227 sheep and 762 goats) sampled in 30 of the 111 Malagasy districts, selected to be representative of the different ecozones and livestock density areas. Sera of animals were tested for the detection of immunoglobulins M (IgM) and G (IgG) against RVFV using commercial enzyme-linked immunosorbent assays kits. Recent infections (presence of IgM against RVFV) were detected in only 9 cattle (0.3% [0.1-0.4]) and 33 small ruminant (3.3% [2.2-4.5]) samples. Past infections (presence of IgG and absence of IgM against RVFV) were detected in 887 cattle (25.8% [24.3-27.3]) and 244 small ruminant (24.7% [22.0-27.4]) samples. Past infections were detected in all sampled sites. All ecozones were affected. In the southern and northwestern areas, the prevalence of cattle showing evidence of past infection with RVFV increased with the age of the animals. Our results suggest that there has been country-wide circulation of RVFV in 2008 in Madagascar, including in parts of the country where no clinical illness, either in animals or in humans, was reported. The data also suggest that the southern and northwestern areas may be endemic for RVFV, and that the virus may spread when ecological conditions are favorable for its amplification.

In Burkina Faso, Glossina palpalis gambiensis Vanderplank and G. tachinoides Westwood (Diptera: Glossinidae) are the main cyclic vectors of trypanosomiasis. The vegetation type along river banks is an important factor determining the distribution and abundance of these tsetse. The following work investigated the relation between the plant species present (including the disturbance level) and tsetse distribution and abundance, using three ecotypes, described by P.C. Morel in 1978. These were the Guinean, Sudano-Guinean and Sudanese gallery forests. In the Mouhoun River basin, these three ecotypes are found successively from upstream to downstream. Berlinia grandiflora, Syzygium guineense and Cola laurifolia and finally Acacia seyal and Mitragyna inermis were the best indicators for the Guinean, Sudano-Guinean and Sudanese gallery forest ecotypes, respectively, as suggested by Morel. However, other species such as Pterocarpus santalinoides and Mimosa pigra were not ecotype specific. Trap catches confirmed that G. palpalis and G. tachinoides are predominant in Guinean and Sudanese gallery forests, respectively, and that both species are well represented in the Sudano-Guinean ecotype. Tsetse densities dropped significantly in disturbed Sudano-Guinean and Sudanese gallery forest sites. However, this was not the case for both species in Guinean or for G. tachinoides in half-disturbed Sudanese gallery forest sites, confirming their high resilience to human-made changes. The importance of a detailed consideration of riverine ecotypes when predicting tsetse densities is discussed.

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Background: Although malaria disappeared from southern France more than 60 years ago, suspicions of recent autochthonous transmission in the French Mediterranean coast support the idea that the area could still be subject to malaria transmission. The main potential vector of malaria in the Camargue area, the largest river delta in southern France, is the mosquito Anopheles hyrcanus (Diptera: Culicidae). In the context of recent climatic and landscape changes, the evaluation of the risk of emergence or re-emergence of such a major disease is of great importance in Europe. When assessing the risk of emergence of vector-borne diseases, it is crucial to be able to characterize the arthropod vector's spatial distribution. Given that remote sensing techniques can describe some of the environmental parameters which drive this distribution, satellite imagery or aerial photographs could be used for vector mapping.