A review of foot-and-mouth disease in Ethiopia: epidemiological aspects, economic implications, and control strategies

A unified Foot and Mouth Disease dataset for Uganda: evaluating machine learning predictive performance degradation under varying distributions

In Uganda, the absence of a unified dataset for constructing machine learning models to predict Foot and Mouth Disease outbreaks hinders preparedness. Although machine learning models exhibit excellent predictive performance for Foot and Mouth Disease outbreaks under stationary conditions, they are susceptible to performance degradation in non-stationary environments. Rainfall and temperature are key factors influencing these outbreaks, and their variability due to climate change can significantly impact predictive performance. This study created a unified Foot and Mouth Disease dataset by integrating disparate sources and pre-processing data using mean imputation, duplicate removal, visualization, and merging techniques. To evaluate performance degradation, seven machine learning models were trained and assessed using metrics including accuracy, area under the receiver operating characteristic curve, recall, precision and F1-score. The dataset showed a significant class imbalance with more non-outbreaks than outbreaks, requiring data augmentation methods. Variability in rainfall and temperature impacted predictive performance, causing notable degradation. Random Forest with borderline SMOTE was the top-performing model in a stationary environment, achieving 92% accuracy, 0.97 area under the receiver operating characteristic curve, 0.94 recall, 0.90 precision, and 0.92 F1-score. However, under varying distributions, all models exhibited significant performance degradation, with random forest accuracy dropping to 46%, area under the receiver operating characteristic curve to 0.58, recall to 0.03, precision to 0.24, and F1-score to 0.06. This study underscores the creation of a unified Foot and Mouth Disease dataset for Uganda and reveals significant performance degradation in seven machine learning models under varying distributions. These findings highlight the need for new methods to address the impact of distribution variability on predictive performance.

Assessing farmers' willingness to pay for FMD vaccines and factors influencing payment: a contingent valuation study in central Oromia, Ethiopia

BACKGROUND

Foot and mouth disease is a contagious, transboundary, and economically devastating viral disease of cloven-hoofed animals. The disease can cause many consequences, including decreased productivity, limited market access, and elimination of flocks or herds. This study aimed to assess farmers' willingness to pay (WTP) for foot and mouth disease (FMD) vaccines and identify factors influencing their WTP. A cross-sectional questionnaire survey was conducted on 396 randomly selected livestock-owning farmers from three districts in the central Oromia region (Ambo, Dendi, and Holeta districts. The study utilized the contingent valuation method, specifically employing dichotomous choice bids with double bounds, to evaluate the willingness to pay (WTP) for the FMD vaccine. Mean WTP was assessed using interval regression, and influential factors were identified.

RESULTS

The study revealed that the farmer's mean willingness to pay for a hypothetical foot and mouth disease vaccine was 37.5 Ethiopian Birr (ETB) [95% confidence interval [CI]: 34.5 40.58] in all data, while it was 23.84 (95% CI: 21.47-26.28) in the mixed farming system and 64.87 Ethiopian Birr (95% CI: 58.68 71.15) in the market-oriented farming system. We identified main livelihood, management system, sales income, breed, keeping animals for profit, and foot and mouth disease impact perception score as significant variables (p ≤ 0.05) determining the farmers' WTP for the FMD vaccine.

CONCLUSION

Farmers demonstrated a high computed willingness to pay, which can be considered an advantage in the foot and mouth disease vaccination program in central Oromia. Therefore, it is necessary to ensure sufficient vaccine supply services to meet the high demand revealed.

Adoption of veterinary vaccines, determining factors, and barriers in Southwest Ethiopia: Implications for livestock health and disease management strategies

In Ethiopia, the use of veterinary vaccines to control animal diseases is an effective strategy. A study conducted in Southwest Ethiopia from October 2020 to October 2021 aimed to determine the adoption level of veterinary vaccines and factors affecting their use. The study used multistage random sampling to select districts and interviewed 476 farmers who had either adopted or not adopted the vaccines. The study found that certain diseases should be prioritized for vaccination to safeguard the health of cattle, sheep, goats, and poultry. These include anthrax (19.12 %), blackleg (17.65 %), foot and mouth disease (10.50 %), and lumpy skin disease (8.82 %) in cattle, and pasteurellosis (18.07 %), contagious caprine pleuropneumonia (15.97 %), peste des petits ruminants (14.15 %), and Orf (13.45 %) in sheep and goats. Newcastle disease (21.85 %), infectious bursal disease (19.33 %), and coccidiosis (17.02 %) were identified as high-priority diseases for flock health. Overall, 30.7 % of farmers were adopters of veterinary vaccines, while 69.3 % were non-adopters. The study identified several factors that influence the likelihood of adopting veterinary vaccines, including breed type (OR = 9.1, p < 0.0001), production size (OR = 9.7, p < 0.0001), production type (OR = 2.7, p < 0.0001), and farm location (OR = 9.8, p = 0.001). Common barriers to vaccination included a lack of disease knowledge, high vaccine costs, limited vaccine availability, and administration difficulties. Insights from the study can guide strategies for promoting veterinary vaccine adoption in Ethiopia. Stakeholders should pay attention to these findings since vaccine use is crucial for controlling animal diseases, enhancing animal health, and preventing economic losses. Further research is needed to investigate factors affecting enhanced veterinary vaccine adoption.