A Data Simulation Method to Optimize a Mechanistic Dose-Response Model for Viral Loads of Hepatitis A

Area of Science:

• Environmental microbiology
• Public health
• Infectious disease modeling

Background:

• Quantitative microbial risk assessment (QMRA) relies on dose-response models to estimate health risks.
• Existing QMRA models require dose data in infectious particles, but Hepatitis A (Hep-A) data is in grams of feces.
• A conversion method is needed to bridge this data gap and improve Hep-A risk assessment.

Purpose of the Study:

• To develop a novel method for converting Hep-A dose data from grams of feces to infectious particles.
• To optimize mechanistic dose-response models for Hep-A using this new conversion method.
• To create a more accurate and computationally rigorous Hep-A risk model.

Main Methods:

• Coupled data simulation with likelihood estimation for model optimization.
• Used data simulation to model fecal doses as viral particles, incorporating within-group variability.
• Applied simulated doses alongside original dose-response data to refine mechanistic models.

Main Results:

• Successfully developed a method to convert Hep-A dose data from grams of feces to infectious particles.
• Optimized Hep-A dose-response models using the novel conversion technique.
• Achieved a more computationally rigorous approach to modeling dose-response data.

Conclusions:

• The developed method provides a robust way to handle Hep-A dose-response data in grams of feces.
• The new approach leads to a more appropriate and accurate dose-response model for Hep-A risk assessment.
• This advancement enhances the utility of QMRA for public health interventions related to Hep-A.