Progress and research trends in lumpy skin disease based on the scientometric assessment

Progress and research trends in lumpy skin disease based on the scientometric assessment – a review

Published:2024-09-07 Issue: Volume: Page:
ISSN:2300-8733
Container-title:Annals of Animal Science
language:en
Short-container-title:

Author:

Zeeshan Hafiz Muhammad¹²,Heyat Md Belal Bin³,Hayat Mohd Ammar Bin⁴,Parveen Saba⁵,Sultana Arshiya⁶,Akhtar Faijan³⁷,Iqbal Abid⁸,Ali Ahmad⁹,Pomary Dustin¹⁰,Ogunsakin Ropo Ebenezer¹¹,Abdelgeliel Asmaa Sayed¹²

Affiliation:

1. Department of Computer Science , National College of Business Administration & Economics , Lahore , Pakistan

2. Department of Computer Science , Superior University , , Lahore , Pakistan

3. CenBRAIN Neurotech Center of Excellence, School of Engineering , Westlake University , Hangzhou, Zhejiang , China

4. College of Intelligent Systems Science and Engineering , Harbin Engineering University , Harbin , China

5. College of Electronics and Information Engineering , Shenzhen University , Shenzhen, Guangdong 518060 , China

6. Department of Ilmul Qabalat wa Amraze Niswan , National Institute of Unani Medicine, Ministry of AYUSH , Bengaluru , India

7. School of Computer Science and Engineering , University of Electronic Science and Technology of China , Chengdu , China

8. Central Library , Prince Sultan University , Riyadh , Kingdom of Saudi Arabia

9. College of Computer Science and Software Engineering , Shenzhen University , Shenzhen , China

10. Electrical/Electronics Engineering Department , Ho Technical University , Ho, Volta Region , Ghana

11. Department of Medicine , Western University , London Ontario , Canada

12. Department of Botany & Microbiology , South Valley University , Qena , Egypt

Abstract

Abstract Background Lumpy skin disease (LSD) has been a significant concern in veterinary medicine since its discovery. Despite decades of research, understanding the full spectrum of this disease remains a challenge. To address this gap, a comprehensive analysis of the existing body of knowledge on LSD is essential. Bibliometric analysis offers a systematic approach towards the mapping of research landscape, identifying key contributors, and uncovering emerging trends in LSD research. Objective This study aims to conduct a thorough bibliometric analysis spanning from 1947 to till the present date in order to map the knowledge domain of LSD. The objective is to gain insights into the global research trends, identify influential contributors, explore collaboration networks, and predict future outlook in LSD research. Method Data extracted from the Scopus database was used to perform a bibliometric analysis. 341 relevant documents were selected for analysis. Bibliometric indicators, including publication numbers, citation counts, and the h-index, were utilized to assess the comprehensive contributions of nations, organizations, authors, and source titles. Additionally, cooperation networks between countries, organizations, and authors were visualized using the VOSviewer tool. Results The analysis revealed a significant increase in research output on LSD, with a notable growth rate of 19.26%. Since its discovery in Zambia in 1929, LSD research has grown steadily, with an average annual growth rate of 5.21%. The University of Pretoria and the Federal Centre for Animal Health emerged as the most active institutions and organizations in LSD research. The Journal of Virology was identified as the most cited journal, reflecting its significant impact on the field, and a strong international collaboration was observed between the United Kingdom and South Africa. Conclusion This study provides valuable insights into the global research landscape of LSD, highlighting key trends, contributors, and collaboration networks. By reviewing decades of research, this analysis enhances our understanding of LSD and serves as a foundation for future research endeavours. The findings of this study will aid researchers in navigating the vast literature on LSD, ultimately contributing to advancements in veterinary medicine and disease management strategies.

Publisher

Walter de Gruyter GmbH

Link

https://www.sciendo.com/pdf/10.2478/aoas-2024-0091

Reference76 articles.

1. Akhtar F., Bin Heyat M.B., Li J.P., Patel P.K., Rishipal., Guragai B. (2020). Role of Machine Learning in Human Stress: A Review. 2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing, 170–174.

2. Akhtar F., Heyat M.B.B., Sultana A., Parveen S., Zeeshan H.M., Stalin Fathima M., Shen B., Pomary D., Li J.P., Sawan M., Belal Bin Heyat M., Sultana A., Parveen S., Zeeshan H.M., Stalin Fathima M., Shen B. (2024). Medical intelligence for anxiety research: Insights from genetics, hormones, implant science, and smart devices with future strategies. WIREs Data Mining and Knowledge Discovery, e1552.

3. Akhtar F., Li J.P., Heyat M.B.B., Quadri S.L., Ahmed S.S., Yun X., Haq A.U. (2019). Potential of Blockchain Technology in Digital Currency: A Review. 2019 16th International Computer Conference on Wavelet Active Media Technology and Information Processing, 85–91.

4. Akhtar F., Patel P.K., Heyat M.B.B., Yousaf S., Baig A.A., Mohona R.A., Mutoffar M.M., Bhattacharya T., Teelhawod B.N., Li J.P., Kamal M.A., Wu K. (2022). Smartphone Addiction among Students and its Harmful Effects on Mental Health, Oxidative Stress, and Neurodegeneration towards Future Modulation of Anti-Addiction Therapies: A Comprehensive Survey based on SLR, Research Questions, and Network Visualization. CNS & Neurological Disorders - Drug Targets, 22: 1070–1089.

5. Alphonse A.S., Benifa J.V.B., Muaad A.Y., Chola C., Heyat M.B.B., Murshed B.A.H., Abdel Samee N., Alabdulhafith M., Al-antari M.A. (2023). A Hybrid Stacked Restricted Boltzmann Machine with Sobel Directional Patterns for Melanoma Prediction in Colored Skin Images. Diagnostics, 13.