From Trait-Structured Within-Host Dynamics to SIR Models: A Multiscale Framework With Re-Exposure

Area of Science:

• Mathematical Biology
• Epidemiology
• Immunology

Background:

• Understanding the link between within-host pathogen dynamics and population-level disease spread is crucial for effective public health strategies.
• Existing multiscale models often use ad hoc linking functions, limiting their mechanistic consistency.
• Heterogeneity in immune responses and pathogen inoculum size can significantly influence infection outcomes.

Purpose of the Study:

• To develop a unified, threshold-based multiscale framework connecting within-host infection dynamics to a structured population model.
• To derive population compartments directly from within-host trajectories, avoiding artificial linking mechanisms.
• To investigate the impact of inoculum thresholds and immune activation on disease dynamics and epidemic indicators.

Main Methods:

• Developed a two-variable within-host model for pathogen load and immune response, incorporating Allee-like inoculum thresholds and nonlinear immune activation.
• Derived mapping rules to classify continuous within-host trajectories into susceptible, infected (low/high immune protection), and recovered states.
• Formulated a next-generation operator for trait-structured re-exposure and an explicit R0 expression for global mixing scenarios.

Main Results:

• Simulations demonstrated sharp transitions between pathogen clearance and persistence, influenced by inoculum size and immune status.
• Observed an emergent cascade: susceptible -> infected (low immunity) -> infected (high immunity) -> recovered.
• Chronic within-host equilibria sustained infection even when the basic reproduction number (R0) was less than 1, exhibiting backward-bifurcation-like population dynamics.

Conclusions:

• The framework provides a consistent method to link immunological heterogeneity to epidemic indicators.
• Identified potential for chronic infection reservoirs and backward-bifurcation phenomena driven by within-host dynamics.
• Enables estimation of disease control thresholds directly from within-host measurements, improving dose-response data interpretation.