Predicting the abundance of African horse sickness vectors in South Africa using GIS and artificial neural networks

Eksteen, Sanet Patricia; Breetzke, Gregory Dennis

doi:10.4102/sajs.v107i7/8.404

Cited by 11 publications

(18 citation statements)

References 6 publications

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“…The most important variables impacting its abundance were the NDVI, livestock density and temperature. These three types of variables have also been linked to C. bolitinos abundance in South Africa [ 66 ]. Livestock density was an important variable which greatly impacted model accuracy.…”

Section: Discussionmentioning

confidence: 99%

Spatial distribution modelling of Culicoides (Diptera: Ceratopogonidae) biting midges, potential vectors of African horse sickness and bluetongue viruses in Senegal

Diarra

Fall

et al. 2018

Parasites Vectors

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BackgroundIn Senegal, the last epidemic of African horse sickness (AHS) occurred in 2007. The western part of the country (the Niayes area) concentrates modern farms with exotic horses of high value and was highly affected during the 2007 outbreak that has started in the area. Several studies were initiated in the Niayes area in order to better characterize Culicoides diversity, ecology and the impact of environmental and climatic data on dynamics of proven and suspected vectors. The aims of this study are to better understand the spatial distribution and diversity of Culicoides in Senegal and to map their abundance throughout the country.MethodsCulicoides data were obtained through a nationwide trapping campaign organized in 2012. Two successive collection nights were carried out in 96 sites in 12 (of 14) regions of Senegal at the end of the rainy season (between September and October) using OVI (Onderstepoort Veterinary Institute) light traps. Three different modeling approaches were compared: the first consists in a spatial interpolation by ordinary kriging of Culicoides abundance data. The two others consist in analyzing the relation between Culicoides abundance and environmental and climatic data to model abundance and investigate the environmental suitability; and were carried out by implementing generalized linear models and random forest models.ResultsA total of 1,373,929 specimens of the genus Culicoides belonging to at least 32 different species were collected in 96 sites during the survey. According to the RF (random forest) models which provided better estimates of abundances than Generalized Linear Models (GLM) models, environmental and climatic variables that influence species abundance were identified. Culicoides imicola, C. enderleini and C. miombo were mostly driven by average rainfall and minimum and maximum normalized difference vegetation index. Abundance of C. oxystoma was mostly determined by average rainfall and day temperature. Culicoides bolitinos had a particular trend; the environmental and climatic variables above had a lesser impact on its abundance. RF model prediction maps for the first four species showed high abundance in southern Senegal and in the groundnut basin area, whereas C. bolitinos was present in southern Senegal, but in much lower abundance.ConclusionsEnvironmental and climatic variables of importance that influence the spatial distribution of species abundance were identified. It is now crucial to evaluate the vector competence of major species and then combine the vector densities with densities of horses to quantify the risk of transmission of AHS virus across the country.Electronic supplementary materialThe online version of this article (10.1186/s13071-018-2920-7) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Spatial distribution modelling of Culicoides (Diptera: Ceratopogonidae) biting midges, potential vectors of African horse sickness and bluetongue viruses in Senegal

Diarra

Fall

et al. 2018

Parasites Vectors

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“…ANN, according to several authors [3][4][5][6][7][8][9][11][12][13][14][15], is a promising tool in analyzing and predicting lots of complex issues that exist in animal studies. As indicated by Fernández et al [10], its advantage over traditional analytical methods is due to its accuracy of estimation as well as its ability to generalize even when less significant data is entered.…”

Section: Discussionmentioning

confidence: 99%

“…Artificial neural networks have found their use in several fields of animal husbandry. The designing of an appropriate model that takes account of measurable and immeasurable features offers possibilities for, amongst others, forecasting the occurrence of lameness in horses [3], assessing animal behaviors while estimating their levels of welfare [9], analyzing the factors impacting on milk yield in cows [4] and goats [10], susceptibility to mastitis in cattle [6,11] as well as the risk of occurrence of complications in parturition [12], predicting the occurrence of African horse sickness [13], in genomic selection in cattle [14], and including research in the field of evolution [15]. Data analysis with the use of artificial neural networks would pave the way for precise classification and adjustments to the expected pattern, clustering, modelling, and forecasting.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, this study that relies on artificial neural networks can become a crucially interesting tool which will allow the identification and analysis of features that significantly influence the utility values of Hucul horses and also significantly shorten the time necessary to obtain such information. The approach proposed for the manuscript is novel, as previous works using ANN on equines had focused on singular problems, for example lameness (3), colic (5), and African horse sickness (13). The use of ANN enables objective assessments of individual animals by taking into account only factors essential for determining horses' performance and breeding values.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting the Utility Value of Hucul Horses by Means of Artificial Intelligence

Topczewska

Kwater

2020

Sustainability

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The paper suggests the application of artificial neural networks (ANN) for the analysis of variables that significantly impact on the results of Hucul horses that participate at the National Breeding and Utility Championships for Hucul horses. The study exploits the results obtained during 2009–2015. The research material collected enabled the creation of a set of input data (for the artificial neural network), out of which independent learning and testing sets were isolated. The neural classification system in form of a multi-layered artificial neural network suggested in this paper was implemented in the programming environment Matlab, the 8.1.0.604 version. Each horse was described using features in three models. Experimental simulations were carried out separately for each model, conducting the learning and testing simulation process 10 times. In accepting the division of the evaluated group of horses into 10 classes for the analysis of the issue both the expert and network designated the classes, not without reservations due to imprecision of demarcations. The increase in class numbers would result in increased accuracy of selection (allocation to varied classes) of individuals. The average for 10 network responses which was 77% suggest an identical or a very similar horse class when compared with the expert’s value. Preliminary results of the application of artificial neural networks in predicting the utility value of Hucul horses, relying on a specific set of features seem promising.

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“…Both entomological surveillance data and ecoclimatic variables relating to vector abundance were used to predict Culicoides distribution in Spain as follows. As climate has a delayed effect of 1 month on Culicoides abundance, data about Culicoides traps for a specific month were combined with the climate data for the previous month (Eksteen and Breetzke, ).…”

Section: Methodsmentioning

confidence: 99%

Identification of Suitable Areas for African Horse Sickness Virus Infections in Spanish Equine Populations

Sánchez-Matamoros

Sánchez-Vizcaíno

Rodríguez-Prieto

et al. 2014

Transbound Emerg Dis

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African horse sickness (AHS) is one of the most important vector-borne viral infectious diseases of equines, transmitted mainly by Culicoides spp. The re-emergence of Culicoides-borne diseases in Europe, such as the recent bluetongue (BT) or Schmallenberg outbreaks, has raised concern about the potential re-introduction and further spread of AHS virus (AHSV) in Europe. Spain has one of the largest European equine populations. In addition, its geographical, environmental and entomological conditions favour AHSV infections, as shown by the historical outbreaks in the 1990s. The establishment of risk-based surveillance strategies would allow the early detection and rapid control of any potential AHSV outbreak. This study aimed to identify the areas and time periods that are suitable or at high risk for AHS occurrence in Spain using a GIS-based multicriteria decision framework. Specifically risk maps for AHS occurrence were produced using a weighted linear combination of the main risk factors of disease, namely extrinsic incubation period, equine density and distribution of competent Culicoides populations. Model results revealed that the south-western and north-central areas of Spain and the Balearic Islands are the areas at the highest risk for AHSV infections, particularly in late summer months. Conversely, Galicia, Castile and Leon and La Rioja can be considered as low-risk regions. This result was validated with historical AHS and BT outbreaks in Spain, and with the Culicoides vector distribution area. The model results, together with current Spanish equine production features, should provide the foundations to design risk-based and more cost-effective surveillance strategies for the early detection and rapid control potential of AHS outbreaks in Spain.

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Predicting the abundance of African horse sickness vectors in South Africa using GIS and artificial neural networks

Cited by 11 publications

References 6 publications

Spatial distribution modelling of Culicoides (Diptera: Ceratopogonidae) biting midges, potential vectors of African horse sickness and bluetongue viruses in Senegal

Spatial distribution modelling of Culicoides (Diptera: Ceratopogonidae) biting midges, potential vectors of African horse sickness and bluetongue viruses in Senegal

Forecasting the Utility Value of Hucul Horses by Means of Artificial Intelligence

Identification of Suitable Areas for African Horse Sickness Virus Infections in Spanish Equine Populations

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