Artificial Intelligence in the Control of Food Borne Diseases: A Review

Rithika Pravin Iyer, Ruchika Pravin Iyer, Syeda Ayesha Bushra, Dr Vidya Niranjan

Abstract: Artificial intelligence over time has become a primary resource for predicting the spread of infectious diseases. Its ability to organize information from multiple cases enables in effective epidemic control. The study of pathogens that cause food borne diseases at a genomic level helps in elucidating the nature and course of infection. The primary area of application lies in the determination of the origin of the disease-causing pathogen in food material. Tracking the sale of the food supply is performed using a spatial network model based upon the gravity model that determines the route of transfer in the form of a tree. On the other hand, a machine learning based classifier enables in the prediction of the pathogen’s host. This is performed by studying the whole genome sequence of the pathogen extracted from infected patients which aids in the determination of the infected food source. Both of these techniques will help to curb its spread. This operating procedure can be standardized and used as a template for other infectious diseases that don’t spread through food. This review details the application of artificial intelligence in food borne diseases and how this practice can be extended to other epidemics too.

Keywords: Artificial intelligence, Machine learning, Random Forest Classifier, Food-borne diseases, Gravity model, Convolutional neural network