Determining Techniques for Tracking Foodborne Illnesses and Intoxications in India

Abstract

The objective of this research is to determine the percentage of foodborne infections and intoxications that can be attributed to particular sources in India. Additionally, it provides innovative outlier identification methods utilizing Machine Learning (ML) techniques in conjunction with standard statistical approaches such as ARIMA and SARIMA. The project aims to enhance the precision of identifying the main origins of foodborne infections by integrating epidemiological monitoring, genetic typing, and sophisticated machine learning-based outlier detection. The study findings indicate that more than sixty percent of foodborne diseases may be attributed to contaminated water and raw produce. Improper food handling is responsible for twenty-five percent of these infections, while infected animal products contribute to fifteen percent. These results emphasize the need for improved food safety standards and public awareness initiatives. The study emphasizes the significance of contemporary surveillance systems, which use statistical and machine learning techniques, to monitor and regulate foodborne diseases more. This, in turn, leads to enhanced public health outcomes in India.