Hidden challenges in evaluating spillover risk of zoonotic viruses using machine learning models.

Journal: Communications medicine
Published Date: May 20, 2025

Abstract

BACKGROUND: Machine learning models have been deployed to assess the zoonotic spillover risk of viruses by identifying their potential for human infectivity. However, the lack of comprehensive datasets for viral infectivity poses a major challenge, limiting the predictable range of viruses.

Authors

  • Junna Kawasaki
    Faculty of Science and Engineering, Waseda University, Tokyo, Japan. jrt13mpmuk@gmail.com.
  • Tadaki Suzuki
    Department of Infectious Disease Pathobiology, Graduate School of Medicine, Chiba University, Chiba, Japan.
  • Michiaki Hamada
    Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, 55N-06-10, 3-4-1, Okubo Shinjuku-ku, Tokyo 169-8555, Japan; Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7, Aomi, Koto-ku, Tokyo 135-0064, Japan. Electronic address: mhamada@waseda.jp.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals