The effect of ongoing exposure dynamics in dose response relationships

Area of Science:

• Microbiology
• Epidemiology
• Computational Biology

Background:

• Microbial risk assessment relies on understanding infectivity as a function of pathogen dose.
• Traditional dose-response experiments administer pathogen doses at a single time point.
• Existing models often overlook dose timing and assume pathogen independence, not reflecting real-world exposures.

Purpose of the Study:

• To model pathogen-immune effector interactions during the pre-infection phase.
• To investigate the dynamic effects of dose timing on infection risk.
• To challenge the assumptions of static dose-response models in microbial risk assessment.

Main Methods:

• Developed a dynamic model of immune effector and pathogen interactions.
• Analyzed the relationship between exposure accumulation time and infection risk.
• Utilized Cryptosporidium parvum as a case study for model fitting and prediction.

Main Results:

• An inverse relationship exists between exposure duration and infection risk.
• Single-time dose experiments may overestimate pathogen-specific infection risks.
• Model predictions show a significant reduction in risk with a 100-fold increase in exposure window (0.66 to 0.09 for C. parvum).

Conclusions:

• Dose timing is a critical factor in determining infection risk.
• Dynamic modeling provides a more realistic assessment of pathogen exposure.
• Findings suggest dose timing significantly influences risk for pathogens transmitted via different routes (e.g., airborne vs. fomite).