Bluetongue Disease in Small Ruminants in Palestine: A retrospective Study Between the period of 2005-2019

Alzuheir, Ibrahim; Atalla, Hatem; Hajji, Qais A

doi:10.21608/ejvs.2021.76896.1234

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…Similarly, El-Bagoury and Moneer [ 33 ] discovered that autumn had the highest incidence of BT (21.1%). In contrast, Alzuheir et al [ 34 ] found that the incidence of BT was lowest in the spring and summer (May–June). Based on ANN variable importance, BT risk in sheep is higher than in goats.…”

Section: Discussionmentioning

confidence: 94%

Comparison of machine learning models for bluetongue risk prediction: a seroprevalence study on small ruminants

et al. 2022

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Background Bluetongue (BT) is a disease of concern to animal breeders, so the question on their minds is whether they can predict the risk of the disease before it occurs. The main objective of this study is to enhance the accuracy of BT risk prediction by relying on machine learning (ML) approaches to help in fulfilling this inquiry. Several risk factors of BT that affect the occurrence and magnitude of animal infection with the virus have been reported globally. Additionally, risk factors, such as sex, age, species, and season, unevenly affect animal health and welfare. Therefore, the seroprevalence study data of 233 apparently healthy animals (125 sheep and 108 goats) from five different provinces in Egypt were used to analyze and compare the performance of the algorithms in predicting BT risk. Results Logistic regression (LR), decision tree (DT), random forest (RF), and a feedforward artificial neural network (ANN) were used to develop predictive BT risk models and compare their performance to the base model (LR). Model performance was assessed by the area under the receiver operating characteristics curve (AUC), accuracy, true positive rate (TPR), false positive rate (FPR), false negative rate (FNR), precision, and F1 score. The results indicated that RF performed better than other models, with an AUC score of 81%, ANN of 79.6%, and DT of 72.85%. In terms of performance and prediction, LR showed a much lower value (AUC = 69%). Upon further observation of the results, it was discovered that age and season were the most important predictor variables reported in classification and prediction. Conclusion The findings of this study can be utilized to predict and control BT risk factors in sheep and goats, with better diagnostic discrimination in terms of accuracy, TPR, FNR, FPR, and precision of ML models over traditional and commonly used LR models. Our findings advocate that the implementation of ML algorithms, mainly RF, in farm decision making and prediction is a promising technique for analyzing cross-section studies, providing adequate predictive power and significant competence in identifying and ranking predictors representing potential risk factors for BT.

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Section: Discussionmentioning

confidence: 94%

Comparison of machine learning models for bluetongue risk prediction: a seroprevalence study on small ruminants

et al. 2022

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“…More than 53 mosquito species have been identified as possible vectors for RVFV [26], including Phlebotomus papatasi and Aedes albopictus in Palestine [27,28]. These vectors are related to the endemic circulation of other arboviral diseases in Palestine, like West Nile fever in humans and animals, and bluetongue in ruminants [29,30]. Controlling such a disease requires a strategy like the One Health approach, which involves understanding and managing the animal, human, and environmental determinants of the disease [10].…”

Section: Sample Variables Are Shown In Table-1mentioning

confidence: 99%

“…Understanding the factors responsible for the absence of RVFV could provide valuable information to prevent and control the disease in a region infested by the vector but lacking the virus [25]. Our findings provide a baseline for future prediction and early warning systems for RVFV emergence.< Spatial and temporal surveillance of the vectors and susceptible animals will facilitate early warning [25][26][27][28][29][30][31][32].…”

Section: Sample Variables Are Shown In Table-1mentioning

confidence: 99%

No evidence of Rift Valley fever antibodies in veterinarians and sheep in Northern Palestine

Alzuheir

Helal

et al. 2022

Vet World

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Background and Aim: Rift Valley fever virus (RVFV) is a vector-borne virus that causes RVF in humans and ruminants. The clinical symptoms in humans and animals are non-specific and often misdiagnosed, but abortions in ruminants and high mortality in young animals are characteristic. Since the initial outbreak in the Rift Valley area in Kenya, the disease has spread to most African countries and the Middle East. The presence and epidemiological status of RVFV in humans and animals in Palestine are unknown. This study aimed to investigate the presence and risk factors for RVF seroprevalence in veterinarians, as occupational hazard professionals, and sheep, as highly susceptible animals, in Northern Palestine. Materials and Methods: A cross-sectional study was conducted. Data and blood samples of 280 Assaf sheep and 100 veterinarians in close occupational contact with sheep were collected between August and September 2020 using an indirect enzyme-linked immunosorbent assay. Results: No evidence of RVF antibodies was found in any human or animal sample. Conclusion: Our results suggest that RVFV has not circulated in livestock in Northern Palestine, yet. Surveillance and response capabilities and cooperation with the nearby endemic regions are recommended. The distribution of competent vectors in Palestine, associated with global climate change and the role of wild animals, might be a possible route for RVF spreading to Palestine from neighboring countries.

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Bluetongue Disease in Small Ruminants in Palestine: A retrospective Study Between the period of 2005-2019

Cited by 2 publications

References 19 publications

Comparison of machine learning models for bluetongue risk prediction: a seroprevalence study on small ruminants

Comparison of machine learning models for bluetongue risk prediction: a seroprevalence study on small ruminants

No evidence of Rift Valley fever antibodies in veterinarians and sheep in Northern Palestine

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