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

  • Authors:

  • Lixu Qin;Simon X. Yang;Frank Pollari;Kathryn Dore;Aamir Fazil;Rafiq Ahmed;Jane Buxton;Karen Grimsrud

  • Affiliations:

  • Advanced Robotics and Intelligent Systems (ARIS) Laboratory, School of Engineering, University of Guelph, 50 Stone Road East, Guelph, Ontario, Canada N1G 2W1;Advanced Robotics and Intelligent Systems (ARIS) Laboratory, School of Engineering, University of Guelph, 50 Stone Road East, Guelph, Ontario, Canada N1G 2W1;Foodborne, Waterborne and Zoonotic Infections Division, Public Health Agency of Canada, Guelph, Canada;Foodborne, Waterborne and Zoonotic Infections Division, Public Health Agency of Canada, Guelph, Canada;Laboratory for Foodborne Zoonoses, Public Health Agency of Canada, Guelph, Canada;National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, Canada;British Columbia Centre for Disease Control, Vancouver, Canada;Alberta Health and Wellness, Edmonton, Canada

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2008

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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.