New Zealand has the highest rate of reported campylobacteriosis in the developed world. Due to the large economic and health consequences of campylobacteriosis, prediction models for disease are required to be designed to predict accurately the number of campylobacteriosis cases. The Bayesian approach has gained increased interest in recent years for calculating the outcomes of quantitative microbial risk assessment (QMRA). A classical time series and Monte Carlo (MC) modelling were also explored as appropriate techniques to predict campylobacteriosis. A simplified model representing the entire food chain from the farm to the fork with all the variables of interest was used with the Bayesian method. The Auto Regressive Integrated Moving-Average intervention models (ARIMA additive and multiplicative), Holt-Winters (HW multiplicative) and Bayesian methods were considered the best models for predicting the actual 7333 notified campylobacteriosis cases with 7990, 8442, 8666 and 9250 cases, respectively. It is also noteworthy that the notification rate has more or less stabilised since 2008 until 2017. MC modelling provided the least realistic prediction (846,451 cases). The HW method is simple to use and reliable method for time series predictions. However, the Bayesian method provides a prior assessment of any possible intervention in the food chain and provides satisfactory prediction accuracy in spite of the complexity involved in constructing and assigning probabilities from expert knowledge or prior information, linking the nodes and complex software. This study highlighted the importance of the Bayesian model to assess all the factors which may contribute to the campylobacteriosis risk and confirmed that it can provide better conclusions for QMRA than the MC technique because of its interactive link between the data and the parameter (backward inference).

在发达国家，新西兰的弯曲菌病发生率最高。由于弯曲菌病的巨大经济和健康后果，因此需要设计疾病的预测模型以准确预测弯曲菌病病例的数量。近年来，贝叶斯方法越来越引起人们对计算定量微生物风险评估（QMRA）结果的兴趣。还探讨了经典的时间序列和蒙特卡洛（MC）建模作为预测弯曲菌病的适当技术。贝叶斯方法使用了一个简化模型，该模型代表了从农场到叉子的整个食物链，并包含所有感兴趣的变量。自动回归综合移动平均干预模型（ARIMA加法和乘法），Holt-Winters（HW乘法）和贝叶斯方法被认为是预测7333例通报的弯曲杆菌病病例的最佳模型，分别为7990例，8442例，8666例和9250例。还值得注意的是，自2008年到2017年，通知率或多或少稳定。MC建模提供了最不现实的预测（846,451个案例）。HW方法易于使用，并且是用于时间序列预测的可靠方法。然而，尽管在构建和分配专家知识或先验信息的概率，分配节点和复杂软件的过程中涉及复杂性，贝叶斯方法仍可对食物链中的任何可能干预进行事前评估，并提供令人满意的预测准确性。