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Mathematical and Artificial Intelligence Models for Zoonotic Foodborne Pathogens
Version 1
: Received: 16 August 2022 / Approved: 17 August 2022 / Online: 17 August 2022 (08:57:27 CEST)
A peer-reviewed article of this Preprint also exists.
Pillai, N.; Ramkumar, M.; Nanduri, B. Artificial Intelligence Models for Zoonotic Pathogens: A Survey. Microorganisms 2022, 10, 1911. Pillai, N.; Ramkumar, M.; Nanduri, B. Artificial Intelligence Models for Zoonotic Pathogens: A Survey. Microorganisms 2022, 10, 1911.
Abstract
Globally, zoonotic diseases have been on the rise in recent years. Predictive modelling approaches have been successfully used in the literature to identify the underlying causes of these zoonotic diseases. We examine the latest research in the field of predictive modeling that verifies the growth of zoonotic pathogens and assesses the factors associated with their spread. The results of our survey indicate that popular mathematical models can successfully be used in modeling the growth rate of these pathogens under varying storage temperatures. Additionally, some of them are used for the assessment of the inactivation of these pathogens based on various conditions. Based on the results of our study, machine learning models and deep learning are commonly used to detect pathogens within food items and to predict the factors associated with the presence of the pathogens.
Keywords
zoonotic pathogens; mathematical algorithms; machine learning; deep learning
Subject
Biology and Life Sciences, Animal Science, Veterinary Science and Zoology
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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