Sequential infection experiments for quantifying innate and adaptive immunity during influenza infection

Area of Science:

• Immunology
• Infectious Disease Modeling

Background:

• Laboratory models are crucial for understanding pathogen interactions and host immunity.
• Sequential infection experiments in ferrets have shown variable cross-immunity based on exposure timing and pathogen strains.
• Single infection studies face challenges in determining the relative importance of different immune components.

Purpose of the Study:

• To investigate if sequential infection experiments can identify immune components of cross-protection.
• To determine if this experimental design offers insights into primary immune responses during a single infection.
• To compare the analytical power of sequential versus single infection data.

Main Methods:

• Utilized simulations and mathematical modeling.
• Analyzed virological data from sequential infection experiments.
• Compared data from sequential and single infection models.

Main Results:

• Sequential infection data accurately reveals the timing and extent of cross-protection.
• The immune components responsible for cross-protection can be identified.
• Data from sequential infections can infer primary infection control mechanisms, even without serological data.
• Single infection data is insufficient for reliable inference of these parameters.

Conclusions:

• Sequential infection experiments provide a robust method for dissecting cross-immunity and primary immune responses.
• This experimental design enhances understanding of infection control and resolution mechanisms.
• Previous exposure effects on subsequent infection dynamics are better elucidated through sequential modeling.