Graduate School of Agricultural Science, Hokkaido University, Sapporo, Japan.
Risk Assessment Unit, Finnish Food Authority, Helsinki, Finland.
Appl Environ Microbiol. 2021 Jul 13;87(15):e0091821. doi: 10.1128/AEM.00918-21.
This study was conducted to quantitatively evaluate the variability of stress resistance in different strains of Campylobacter jejuni and the uncertainty of such strain variability. We developed Bayesian statistical models with multilevel analysis to quantify variability within a strain, variability between different strains, and the uncertainty associated with these estimates. Furthermore, we measured the inactivation of 11 strains of C. jejuni in simulated gastric fluid with low pH, using the Weibullian survival model. The model was first developed for separate pH conditions and then analyzed over a range of pH levels. We found that the model parameters developed under separate pH conditions exhibited a clear dependence of survival on pH. In addition, the uncertainty of the variability between different strains could be described as the joint distribution of the model parameters. The latter model, including pH dependency, accurately predicted the number of surviving cells in individual as well as multiple strains. In conclusion, variabilities and uncertainties in inactivation could be simultaneously evaluated and interpreted via a probabilistic approach based on Bayesian theory. Such hierarchical Bayesian models could be useful for understanding individual-strain variability in quantitative microbial risk assessment. Since microbial strains vary in their growth and inactivation patterns in food materials, it is important to accurately predict these patterns for quantitative microbial risk assessment. However, most previous studies in this area have used highly resistant strains, which could lead to inaccurate predictions. Moreover, variability, including measurement errors and variability within a strain and between different strains, can contribute to predicted individual-level outcomes. Therefore, a multilevel framework is required to resolve these levels of variability and estimate their uncertainties. We developed a Bayesian predictive model for the survival of Campylobacter jejuni under simulated gastric conditions taking into account the variabilities and uncertainties. We demonstrated a high correspondence between predictions from the model and empirical measurements. The modeling procedure proposed in this study recommends a novel framework for predicting pathogen behavior, which can help improve quantitative microbial risk assessment during food production and distribution.
本研究旨在定量评估空肠弯曲菌不同菌株的应激抗性变异性及其变异性的不确定性。我们开发了贝叶斯统计模型，采用多层次分析方法来量化菌株内的变异性、不同菌株间的变异性以及这些估计的不确定性。此外，我们使用 Weibull 生存模型测量了 11 株空肠弯曲菌在低 pH 值模拟胃液中的失活动力学。该模型首先针对不同的 pH 值条件进行开发，然后在一系列 pH 值范围内进行分析。我们发现，在单独 pH 值条件下开发的模型参数显示出生存对 pH 值的明显依赖性。此外，不同菌株间变异性的不确定性可以描述为模型参数的联合分布。包含 pH 值依赖性的后者模型可以准确预测单个和多个菌株中存活细胞的数量。总之，基于贝叶斯理论的概率方法可以同时评估和解释失活动力学中的变异性和不确定性。这种分层贝叶斯模型对于理解定量微生物风险评估中的个体菌株变异性可能非常有用。由于微生物菌株在食品材料中的生长和失活动力学模式存在差异，因此对于定量微生物风险评估，准确预测这些模式非常重要。然而，该领域的大多数先前研究都使用了高度抗性的菌株，这可能导致预测不准确。此外，变异性包括测量误差以及菌株内和菌株间的变异性，都可能对预测的个体水平结果产生影响。因此，需要一个多层次框架来解决这些变异性水平并估计其不确定性。我们开发了一种贝叶斯预测模型，用于考虑空肠弯曲菌在模拟胃条件下的生存情况，同时考虑了变异性和不确定性。我们证明了模型预测与经验测量之间的高度一致性。本研究提出的建模程序为预测病原体行为推荐了一种新颖的框架，这有助于改善食品生产和分配过程中的定量微生物风险评估。
