Modelling and risk factor analysis of Salmonella Typhimurium DT104 and non-DT104 infections

Abstract

A clear understanding of risk factors is very important to develop appropriate prevention and control strategies for infection caused by such pathogens as Salmonella (S.) Typhimurium. The objective of this study is to utilise intelligent models to identify significant risk factors for S. Typhimurium DT104 and non-DT104 illness in Canada, and compare findings to those obtained using traditional statistical methods. Previous studies have focused on analysing each risk factor separately using single variable analysis (SVA), or modelling multiple risk factors using statistical models, such as logistic regression (LR) models. In this paper, neural networks and statistical models are developed and compared to determine which method produces superior results. In general, simulation results show that the neural network yields more accurate prediction than the statistical models. The network size, number of training iterations, learning rate, and training sample size in the neural networks are discussed to improve the performance of systems.