Abstract
African horse sickness (AHS) is a disease that is endemic to sub-Saharan Africa and is caused by a virus potentially transmitted by a number of Culicoides species (Diptera: Ceratopogonidae) including Culicoides imicola and Culicoides bolitinos. The strong association between outbreaks of AHS and the occurrence in abundance of these two Culicoides species has enabled researchers to develop models to predict potential outbreaks. A weakness of current models is their inability to determine the relationships that occur amongst the large number of variables potentially influencing the population density of the Culicoides species. It is this limitation that prompted the development of a predictive model with the capacity to make such determinations. The model proposed here combines a geographic information system (GIS) with an artificial neural network (ANN). The overall accuracy of the ANN model is 83 %, which is similar to other stand-alone GIS models. Our predictive model is made accessible to a wide range of practitioners by the accompanying C. imicola and C. bolitinos distribution maps, which facilitate the visualisation of the model’s predictions. The model also demonstrates how ANN can assist GIS in decision-making, especially where the data sets incorporate uncertainty or if the relationships between the variables are not yet known.
Highlights
Geographic information system (GIS) models were first applied in veterinary science in the late 1960s when a GIS model was applied to better understand the spread of foot-and-mouth disease in England.[1]
Whilst GIS models have had some success in predicting the abundance of Culicoides in South Africa[5] and Europe,[6] these models have largely failed to determine the exact nature of the relationship occurring amongst the large number of variables that influence the occurrence of these vectors
The selected model was subsequently used to predict the abundance of C. imicola and C. bolitinos at trap points where there were no counts for particular months of the study period
Summary
Geographic information system (GIS) models were first applied in veterinary science in the late 1960s when a GIS model was applied to better understand the spread of foot-and-mouth disease in England.[1]. (Diptera: Ceratopogonidae), the vectors responsible for the transmission of the viruses that cause African horse sickness (AHS), bluetongue, epizootic haemorrhagic disease and equine encephalosis.[5,6,7] Whilst GIS models have had some success in predicting the abundance of Culicoides in South Africa[5] and Europe,[6] these models have largely failed to determine the exact nature of the relationship occurring amongst the large number of variables that influence the occurrence of these vectors. A potential solution to the problem of a large number of predictor variables and a complicated context lies in artificial neural networks (ANN)
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