An immune memory-structured SIS epidemiological model for hyperdiverse pathogens

Area of Science:

• Epidemiology
• Mathematical Biology
• Immunology

Background:

• Pathogens like Plasmodium falciparum utilize multigene families for antigenic variation, leading to high prevalence despite immunity.
• Existing agent-based models for pathogen strain dynamics are complex and limit broad application.
• Understanding population dynamics in hyperdiverse pathogen systems is crucial for disease control.

Purpose of the Study:

• To develop a novel mathematical model for infectious disease dynamics incorporating pathogen diversity and immune memory.
• To analyze the population dynamics and identify critical transitions in hyperdiverse epidemiological systems.
• To inform intervention strategies by understanding the role of parasite diversity.

Main Methods:

• Formulation of a structured susceptible-infected-susceptible population model in continuous time.
• Classification of individuals by age and exposure to parasite diversity with specific immune memory.
• Analysis of partial-differential equations to study population dynamics and bifurcation structure.

Main Results:

• The model demonstrates alternative steady states and a tipping point linked to transmission intensity.
• A positive feedback loop between parasite genetic diversity and the force of infection drives critical transitions.
• Interventions require drastic diversity reduction to prevent disease resurgence.

Conclusions:

• Explicitly modeling pathogen diversity and specific immune memory is essential for understanding hyperdiverse epidemiological systems.
• Results have broader implications for ecological competition models with diverse trait spaces.
• Intervention strategies must target pathogen diversity to effectively control hyperdiverse infectious diseases.