A neural network model for dose-response of foodborne pathogens

Foodborne infections are a significant cause of morbidity and mortality in human populations. Risk assessment and public health control measures could be greatly enhanced by establishing an accurate relationship between ingested dose and infection probability, and defining minimum infectious doses. In this paper, a novel neural network model is proposed for dose-response of foodborne pathogens. The proposed model assumes a three-layer structure with a fast back-propagation learning algorithm. The model predictions for four available datasets from the literature are compared using six statistical models: log-normal, log-logistic, simple exponential, flexible exponential, Beta-Poisson and Weibull-Gamma. The methods of least square error, maximum likelihood and correlation coefficient are used for the comparison study that shows the neural network model does better than the statistic models. Predictions of dose-response for multiple types of pathogens and dose-response with different host age and gender using neural network models are discussed with simulations. © 2003 Elsevier B.V. All rights reserved.