Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2022 Sep 27;10(10):1911.
doi: 10.3390/microorganisms10101911.

Artificial Intelligence Models for Zoonotic Pathogens: A Survey

Nisha Pillai  1 , Mahalingam Ramkumar  1 , Bindu Nanduri  2
Affiliations
Review

Artificial Intelligence Models for Zoonotic Pathogens: A Survey

Nisha Pillai et al. Microorganisms. .

Abstract

Zoonotic diseases or zoonoses are infections due to the natural transmission of pathogens between species (animals and humans). More than 70% of emerging infectious diseases are attributed to animal origin. Artificial Intelligence (AI) models have been used for studying zoonotic pathogens and the factors that contribute to their spread. The aim of this literature survey is to synthesize and analyze machine learning, and deep learning approaches applied to study zoonotic diseases to understand predictive models to help researchers identify the risk factors, and develop mitigation strategies. Based on our survey findings, machine learning and deep learning are commonly used for the prediction of both foodborne and zoonotic pathogens as well as the factors associated with the presence of the pathogens.

Keywords: deep learning; machine learning; mathematical algorithms; zoonotic pathogens.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
A flowchart illustrating a selection of manuscripts for inclusion in this review based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).
Figure 2
Figure 2
Predictive algorithms and their representation in etiology based studies.
See this image and copyright information in PMC

References

    1. Carlson C.J., Farrell M.J., Grange Z., Han B.A., Mollentze N., Phelan A.L., Rasmussen A.L., Albery G.F., Bett B., Brett-Major D.M., et al. The future of zoonotic risk prediction. Philos. Trans. R. Soc. B. 2021;376:20200358. doi: 10.1098/rstb.2020.0358. - DOI - PMC - PubMed
    1. Cox D.R. The regression analysis of binary sequences. J. R. Stat. Soc. Ser. B Methodol. 1958;20:215–232. doi: 10.1111/j.2517-6161.1958.tb00292.x. - DOI
    1. Ho T.K. Random decision forests; Proceedings of the 3rd International Conference on Document Analysis and Recognition; Montreal, QC, Canada. 14–16 August 1995; pp. 278–282.
    1. Breiman L. Random forests. Mach. Learn. 2001;45:5–32. doi: 10.1023/A:1010933404324. - DOI
    1. Ntampaka P., Niragire F., Nyaga P.N., Habarugira G. Canine gastrointestinal nematodiases and associated risk factors in Kigali city, Rwanda. J. Parasitol. Res. 2021;2021:9956256. doi: 10.1155/2021/9956256. - DOI - PMC - PubMed

LinkOut - more resources