Predicting the abundance of African horse sickness vectors in South Africa using GIS and artificial neural networks
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 th...
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| Format: | Article |
| Language: | English |
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Academy of Science of South Africa
2011-07-01
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| Series: | South African Journal of Science |
| Subjects: | |
| Online Access: | http://192.168.0.118/index.php/sajs/article/view/9825 |
| _version_ | 1818436176130867200 |
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| author | Sanet Eksteen Gregory Breetzke |
| author_facet | Sanet Eksteen Gregory Breetzke |
| author_sort | Sanet Eksteen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T17:04:37Z |
| format | Article |
| id | doaj.art-0b58e24dacd046179e4890f7060c4381 |
| institution | Directory Open Access Journal |
| issn | 1996-7489 |
| language | English |
| last_indexed | 2024-12-14T17:04:37Z |
| publishDate | 2011-07-01 |
| publisher | Academy of Science of South Africa |
| record_format | Article |
| series | South African Journal of Science |
| spelling | doaj.art-0b58e24dacd046179e4890f7060c43812022-12-21T22:53:45ZengAcademy of Science of South AfricaSouth African Journal of Science1996-74892011-07-011077/8Predicting the abundance of African horse sickness vectors in South Africa using GIS and artificial neural networksSanet Eksteen0Gregory Breetzke1Department of Geography, Geoinformatics and Meteorology, University of PretoriaDepartment of Geography, University of CanterburyAfrican 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.http://192.168.0.118/index.php/sajs/article/view/9825African horse sicknessartificial neural networkCulicoidesgeographic information systemGIS model |
| spellingShingle | Sanet Eksteen Gregory Breetzke Predicting the abundance of African horse sickness vectors in South Africa using GIS and artificial neural networks South African Journal of Science African horse sickness artificial neural network Culicoides geographic information system GIS model |
| title | Predicting the abundance of African horse sickness vectors in South Africa using GIS and artificial neural networks |
| title_full | Predicting the abundance of African horse sickness vectors in South Africa using GIS and artificial neural networks |
| title_fullStr | Predicting the abundance of African horse sickness vectors in South Africa using GIS and artificial neural networks |
| title_full_unstemmed | Predicting the abundance of African horse sickness vectors in South Africa using GIS and artificial neural networks |
| title_short | Predicting the abundance of African horse sickness vectors in South Africa using GIS and artificial neural networks |
| title_sort | predicting the abundance of african horse sickness vectors in south africa using gis and artificial neural networks |
| topic | African horse sickness artificial neural network Culicoides geographic information system GIS model |
| url | http://192.168.0.118/index.php/sajs/article/view/9825 |
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