BLUETONGUE - Remote sensing and risk assessment of vector transmitted diseases: bluetongue

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Context and objectives

Bluetongue is a non-contagious infectious disease transmitted by biting midges (Culicoides species). The main vector is Culicoides imicola but other species belong the Culicoides obsoletus complex. The disease causes high mortality in sheep, whilst cattle are host but rarely show symptoms for the serotypes found in the Mediterrenean basin.  The midges are very small and are suspected to spread over long distances by wind. This hypothesis has mainly been tested qualitatively and not quantitatively with the exception of Bishop (2001) and Alba et al (2004).

The objectives of the projects were twofold. The first objective was to determine the distribution of the possible vectors based on modelling.  The aim was to contrast several modelling techniques as well as to determine what the minimum sampling size necessary for modelling should be.

The second objective was to characterise the possible spread of the vector by wind on a quantitative basis.

Project outcome

Expected scientific results

Artificial neural networks are significantly better than LR models, especially when the correlation between the variables is not taken into account. ENFA gives much more detail in the areas where the habitat is more suitable for the vector than in the areas where the habitat is found to be unsuitable.  The models are well suited within the study area but spurious error occur outside the study area. LR/ANN models require absence data in order to function and the artificial creation of these negative points based on literature introduced a bias. However, when this was compared to the outcome of the ENFA approach,  which does not need absence data, this bias tended to be low.
The wind trajectories show a high correlation with the different stages in the epidemics in Greece and Bulgaria. For each of the three years, we could find that based on the different serotypes of bluetongue, underlying wind patterns might explain the evolution of the epidemics.