IDEAS home Printed from https://ideas.repec.org/a/adi/ijbess/v6y2024i6p187-202.html

Identification of diagnostic methods for African swine fever: A systematic literature review

Author

Listed:
  • Steven Lububu

    (Cape Peninsula University of Technology)

  • Michael Twum-Darko

    (Cape Peninsula University of Technology)

Abstract

This review emphasizes the urgent need for effective diagnostic strategies for African swine fever (ASF), a serious disease affecting pig populations worldwide. The aim of the review is to analyze the existing research on ASF diagnostics through a comprehensive literature review, focusing on different diagnostic approaches, including clinical assessments, PCR tests, ELISA, rapid tests and epidemiological models. It examines their sensitivity, specificity and overall performance, addressing challenges such as the varying sensitivity of tests and cross-reactivity. The review highlights both the strengths and limitations of current methods and suggests areas for improvement and standardization. Recommendations are made for future research and the development of innovative diagnostic tools to improve ASF surveillance and control. This study makes a practical contribution by providing a detailed assessment of ASF diagnostic methods from which veterinary scientists and practitioners can benefit. Theoretical contributions include the identification of gaps in ASF diagnostics and the refinement of discussions on diagnostic accuracy and reliability. These findings are consistent with the journal’s focus on infectious diseases and veterinary research and support progress in veterinary medicine and animal welfare.

Suggested Citation

  • Steven Lububu & Michael Twum-Darko, 2024. "Identification of diagnostic methods for African swine fever: A systematic literature review," International Journal of Business Ecosystem & Strategy (2687-2293), Bussecon International Academy, vol. 6(6), pages 187-202, December.
    • Handle: RePEc:adi:ijbess:v:6:y:2024:i:6:p:187-202
    DOI: 10.36096/ijbes.v6i6.647
    as

    Download full text from publisher

    File URL: https://www.bussecon.com/ojs/index.php/ijbes/article/view/647/385
    Download Restriction: no
    File URL: https://doi.org/10.36096/ijbes.v6i6.647
    Download Restriction: no
    File URL: https://libkey.io/10.36096/ijbes.v6i6.647?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jonathan G. Richens & Ciarán M. Lee & Saurabh Johri, 2020. "Improving the accuracy of medical diagnosis with causal machine learning," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    2. Jonathan G. Richens & Ciarán M. Lee & Saurabh Johri, 2020. "Publisher Correction: Improving the accuracy of medical diagnosis with causal machine learning," Nature Communications, Nature, vol. 11(1), pages 1-1, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bangfeng Wang & Yiwei Li & Mengfan Zhou & Yulong Han & Mingyu Zhang & Zhaolong Gao & Zetai Liu & Peng Chen & Wei Du & Xingcai Zhang & Xiaojun Feng & Bi-Feng Liu, 2023. "Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    2. Elisa Ferrari & Luna Gargani & Greta Barbieri & Lorenzo Ghiadoni & Francesco Faita & Davide Bacciu, 2022. "A causal learning framework for the analysis and interpretation of COVID-19 clinical data," PLOS ONE, Public Library of Science, vol. 17(5), pages 1-21, May.
    3. Alexander Lavin & Ciarán M. Gilligan-Lee & Alessya Visnjic & Siddha Ganju & Dava Newman & Sujoy Ganguly & Danny Lange & Atílím Güneş Baydin & Amit Sharma & Adam Gibson & Stephan Zheng & Eric P. Xing &, 2022. "Technology readiness levels for machine learning systems," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    4. Seou Choi & Yannick Salamin & Charles Roques-Carmes & Rumen Dangovski & Di Luo & Zhuo Chen & Michael Horodynski & Jamison Sloan & Shiekh Zia Uddin & Marin Soljačić, 2024. "Photonic probabilistic machine learning using quantum vacuum noise," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    5. Steven Lububu & Michael Twum-Darko, 2024. "Comparative analysis of machine learning algorithms to improve the diagnosis of African swine fever," International Journal of Business Ecosystem & Strategy (2687-2293), Bussecon International Academy, vol. 6(5), pages 121-137, October.
    6. Raafat M Munshi, 2024. "Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction," PLOS ONE, Public Library of Science, vol. 19(1), pages 1-20, January.
    7. Forney Andrew & Mueller Scott, 2022. "Causal inference in AI education: A primer," Journal of Causal Inference, De Gruyter, vol. 10(1), pages 141-173, January.
    8. Katerina Rigana & Ernst C. Wit & Samantha Cook, 2024. "Navigating Market Turbulence: Insights from Causal Network Contagion Value at Risk," Papers 2402.06032, arXiv.org.
    9. Shilin Zheng & Mengdan Li, 2022. "Does aggressive tweeting by the government help to control the COVID‐19 outbreak? Evidence from China," Economics of Transition and Institutional Change, John Wiley & Sons, vol. 30(4), pages 691-713, October.
    10. Lasse Bohlen & Julian Rosenberger & Patrick Zschech & Mathias Kraus, 2025. "Leveraging interpretable machine learning in intensive care," Annals of Operations Research, Springer, vol. 347(2), pages 1093-1132, April.
    11. Soualihou Ngnamsie Njimbouom & Kwonwoo Lee & Jeong-Dong Kim, 2022. "MMDCP: Multi-Modal Dental Caries Prediction for Decision Support System Using Deep Learning," IJERPH, MDPI, vol. 19(17), pages 1-16, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:adi:ijbess:v:6:y:2024:i:6:p:187-202. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Umit Hacioglu (email available below). General contact details of provider: https://edirc.repec.org/data/ibihutr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.