Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

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.

Journal reference: Microorganisms 2022, 10, 1911
DOI: 10.3390/microorganisms10101911

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, Animal Sciences & Zoology

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