In Depth Analysis of Recent Models for Lumpy Skin Disease Virus and Stage Detection: A Comparative Study

Lumpy skin disease (LSD) presents a significant challenge in veterinary medicine, impacting both animal health and the agricultural economy globally in real-time scenarios. Recognizing the urgency of effective LSD virus detection and prediction, this paper provides a comprehensive review of the latest models employed in the analysis of this disease and its stages. Our review process entails a systematic evaluation of these models, focusing on their internal operating characteristics, including accuracy, sensitivity, specificity, and computational efficiency. By adopting a methodical and comparative approach, we identify the strengths and limitations of each model, offering insights into their practical applicability in various settings. A notable advantage of our review lies in its emphasis on multidisciplinary perspectives, integrating findings from veterinary science, epidemiology, and computational modeling. This holistic approach not only enhances the understanding of the models’ performance but also paves the way for interdisciplinary collaborations, crucial for advancing LSD research. Additionally, the review highlights emerging trends and potential areas for future development, such as the incorporation of artificial intelligence and machine learning techniques in disease detection and prediction. The impacts of this work are manifold. Firstly, it offers a valuable resource for researchers and practitioners in the field, guiding them toward more effective and efficient diagnostic and predictive tools. Secondly, it informs policy makers and stakeholders in the agricultural sector, aiding in the formulation of better strategies for disease management and control. Lastly, by advancing the understanding of LSD virus detection and prediction, this paper contributes to the broader goal of safeguarding animal health and securing the sustainability of livestock industries worldwide.