Estimating the temporal and spatial risk of bluetongue related to the incursion of infected vectors into Switzerland

Abstract

Background: The design of veterinary and public health surveillance systems has been improved by the ability to combine Geographical Information Systems (GIS), mathematical models and up to date epidemiological knowledge. In Switzerland, an early warning system was developed for detecting the incursion of the bluetongue disease virus (BT) and to monitor the frequency of its vectors. Based on data generated by this surveillance system, GIS and transmission models were used in order to determine suitable seasonal vector habitat locations and risk periods for a larger and more targeted surveillance program.

The attraction range of the Onderstepoort 220V light trap for Culicoides biting midges as determined under South African field conditions

“…Onderstepoort light traps are routinely used to determine the risk of a virus moving into, becoming established and spreading in an area (Goffredo et al, 2004;Patakakis, 2004;Cagienard et al, 2006;Conte et al, 2007;Meiswinkel et al, 2008;Racloz et al, 2008). In the absence of laboratory colonies Onderstepoort light traps are also used to collect live Culicoides midges for biological studies requiring live specimens (Paweska et al, 2003;2005;Veronesi et al, 2009;Venter et al, 2011b).…”

Spatial analysis of bluetongue cases and vaccination of Swiss cattle in 2008 and 2009

“…Local environmental conditions and microclimate affect the presence of midges, and the abundance of Culicoides midges varies significantly between different regions of Switzerland (Cagienard et al, 2006;Casati et al, 2009). Due to the environmental conditions, north and northwest of Switzerland is more suitable for Culicoides habitation (Racloz et al, 2007(Racloz et al, , 2008. The lower relative risk of BTV-8 infection in the south of Switzerland was explained, at least partly, by the higher altitude in the mountain area in the south, and the effect of the latitude became non-significant when the altitude was adjusted for in the model.…”

“…1) were excluded from the analysis at regional level since the risk factors associated with BT are believed to differ considerably from the rest of the country in these areas due to the distinct environmental conditions observed in the Alps (Racloz et al, 2008). In the Alps, the microclimate varies within short distances, making it difficult to form a covariate that is valid for the whole region to adjust for the local environmental conditions.…”

New tools for epidemiology: a space odyssey

“…As a consequence, spatio-temporal modelling is increasingly applied for a wide variety of arthropod and snail-borne diseases affecting both humans and animals, e.g. dengue fever (pongsumpun et al 2008, Tipayamongkholgul & Lisakulruk 2011), leishmaniasis (Ready 2008, Brandão-Filho et al 2011, malaria (Mabaso et al 2006, Dongus et al 2009, Dogan et al 2010, Rift Valley fever (Vignolles et al 2009), bluetongue (Racloz et al 2008, willgert et al 2011), schistosomiasis (wu et al 2007, Hu et al 2010, Martins-Bedê et al 2010, food-borne trematode infections (Suwannatrai et al 2011) and plague (Rahelinirina et al 2010). As mentioned above, applications even include historic epidemics (Yu & Christakos 2006).…”