Bayesian Generalized Linear Model for Simulating Bacterial Inactivation/Growth Considering Variability and Uncertainty

Conventional regression analysis using the least-squares method has been applied to describe bacterial behavior logarithmically. However, only the normal distribution is used as the error distribution in the least-squares method, and the variability and uncertainty related to bacterial behavior are not considered. In this paper, we propose Bayesian statistical modeling based on a generalized linear model (GLM) that considers variability and uncertainty while fitting the model to colony count data. We investigated the inactivation kinetic data of Bacillus simplex with an initial cell count of 10(5) and the growth kinetic data of Listeria monocytogenes with an initial cell count of 10(4). The residual of the GLM was described using a Poisson distribution for the initial cell number and inactivation process and using a negative binomial distribution for the cell number variation during growth. The model parameters could be obtained considering the uncertainty by Bayesian inference. The Bayesian GLM successfully described the results of over 50 replications of bacterial inactivation with average of initial cell numbers of 10(1), 10(2), and 10(3) and growth with average of initial cell numbers of 10(-1), 10(0), and 10(1). The accuracy of the developed model revealed that more than 90% of the observed cell numbers except for growth with initial cell numbers of 10(1) were within the 95% prediction interval. In addition, parameter uncertainty could be expressed as an arbitrary probability distribution. The analysis procedures can be consistently applied to the simulation process through fitting. The Bayesian inference method based on the GLM clearly explains the variability and uncertainty in bacterial population behavior, which can serve as useful information for risk assessment related to food borne pathogens.

Automated summary

The proposed Bayesian statistical modeling based on a generalized linear model (GLM) successfully considers variability and uncertainty in bacterial behavior. The accuracy of the model revealed that more than 90% of observed cell numbers were within the 95% prediction interval, providing useful information for risk assessment related to food borne pathogens. The Bayesian inference method based on the GLM explains the variability and uncertainty in bacterial population behavior, demonstrating its applicability in simulation processes.