Driven by the quantitative estimate of risk via the dose-response models, quantitative microbial risk assessment has been used successfully for public health interventions. The dose-response models are derived starting from an average exposed dose of infectious particles, this dictates the dose data units required. Then dose-response data from animal model experiments are used to optimize these mechanistic dose-response models. For hepatitis A (Hep-A), the only available dose-response data use grams of feces for dose units. Therefore, to develop a dose-response model for Hep-A a method of converting these doses in grams of feces into infectious particles, while accounting for the uncertainty of this conversion is needed. This research develops a method to couple data simulation with the likelihood estimation method for model optimization to accomplish this. This adapted method uses data simulation to model the doses as viruses while accounting for the within-group variability of this simulation. Then these simulated doses, coupled with the original dose-response data, are used to optimize the mechanistic dose-response models. This method results in a more computationally rigorous means of modeling these types of dose-response data. The resulting dose-response model for Hep-A is also more appropriate to use than the current option for Hep-A risk models.

在通过剂量反应模型对风险进行定量估计的推动下，定量微生物风险评估已成功用于公共卫生干预。剂量反应模型是从传染性颗粒的平均暴露剂量开始推导出来的，这决定了所需的剂量数据单位。然后使用来自动物模型实验的剂量反应数据来优化这些机械剂量反应模型。对于甲型肝炎 (Hep-A)，唯一可用的剂量反应数据使用粪便克数作为剂量单位。因此，为了开发 Hep-A 的剂量反应模型，需要一种将这些以粪便克数为单位的剂量转换为传染性颗粒的方法，同时考虑到这种转换的不确定性。本研究开发了一种将数据模拟与似然估计方法结合起来进行模型优化的方法来实现这一点。这种改编的方法使用数据模拟将剂量建模为病毒，同时考虑到该模拟的组内变异性。然后，这些模拟剂量与原始剂量反应数据相结合，用于优化机械剂量反应模型。这种方法导致对这些类型的剂量反应数据建模的计算方法更加严格。与 Hep-A 风险模型的当前选项相比，由此产生的 Hep-A 剂量反应模型也更适合使用。结合原始剂量反应数据，用于优化机械剂量反应模型。这种方法导致对这些类型的剂量反应数据建模的计算方法更加严格。与 Hep-A 风险模型的当前选项相比，由此产生的 Hep-A 剂量反应模型也更适合使用。结合原始剂量反应数据，用于优化机械剂量反应模型。这种方法导致对这些类型的剂量反应数据建模的计算方法更加严格。与 Hep-A 风险模型的当前选项相比，由此产生的 Hep-A 剂量反应模型也更适合使用。