Check your assumptions: Further scrutiny of basic model frameworks of antimicrobial resistance

Area of Science:

• Mathematical modeling
• Infectious disease dynamics
• Antimicrobial resistance (AMR)

Background:

• Concerns over antimicrobial resistant (AMR) organisms have increased since the mid-1990s.
• Mathematical models are increasingly used to study the inter-host transmission of AMR infections.
• Previous work categorized AMR models based on assumptions dictating strain coexistence.

Purpose of the Study:

• To perform stability analyses and deterministic/stochastic simulations of existing AMR transmission models.
• To investigate how different model assumptions influence equilibria, stability, and disease burden.
• To explore the impact of superinfection and pathogen fitness costs on AMR dynamics.

Main Methods:

• Stability analysis of mathematical frameworks.
• Deterministic simulations of model dynamics.
• Stochastic simulations of model dynamics.

Main Results:

• Stability analyses revealed shared equilibria but differing stability across models based on assumptions.
• Deterministic simulations showed a discontinuous increase in disease burden beyond a specific antimicrobial treatment threshold, influenced by AMR's fitness cost.
• Superinfection can favor strain coexistence by biasing against resident strains.
• Stochastic simulations indicated that initial conditions can lead models to different equilibria compared to deterministic approaches.

Conclusions:

• Model assumptions critically influence the prediction of AMR dynamics and strain competition.
• Understanding the interplay between treatment thresholds, fitness costs, and disease burden is crucial for effective AMR management.
• Stochastic effects and initial conditions play a significant role in long-term model outcomes.