Two-strain competition in quasineutral stochastic disease dynamics

Area of Science:

• Mathematical Biology
• Epidemiology
• Statistical Physics

Background:

• Stochastic competition models are crucial for understanding disease dynamics.
• Previous studies analyzed susceptible-infected-susceptible (SIS) models, but extensions to more complex scenarios are needed.
• The behavior of competing strains in susceptible-infected-recovered (SIR) models with population turnover remains largely unexamined.

Purpose of the Study:

• To develop a novel perturbation method for analyzing quasineutral competition in stochastic models.
• To apply this method to two-strain epidemic models, specifically extending existing SIS models and introducing a new SIR model.
• To investigate the fixation dynamics of competing strains under identical basic reproduction numbers.

Main Methods:

• Development of a perturbation method to study quasineutral competition.
• Application to two-strain susceptible-infected-susceptible (SIS) and susceptible-infected-recovered (SIR) models.
• Analysis of fixation probabilities and dynamics near the deterministic coexistence line (CL).

Main Results:

• In infinite populations with identical reproduction numbers, systems approach the coexistence line, with noise driving fixation/extinction.
• The perturbation method tracks the probability distribution dynamics near the CL.
• While the slower strain may have a typical advantage, the faster strain is more likely to dominate when both are introduced into a susceptible population.

Conclusions:

• The developed perturbation method provides new insights into fixation dynamics in stochastic competition models.
• The study highlights the critical role of noise and strain characteristics (speed) in determining competitive outcomes.
• Findings are relevant for understanding pathogen evolution and disease control strategies.