Modelling and calibrating antibiotic resistance dynamics with uncertainty: a particle swarm optimization approach for estimating random parameters distribution
Mathematical modeling demonstrates how antibiotic resistance evolves in Acinetobacter baumannii, indicating robust calibration under uncertainty.
Key Points
This work presents a mathematical model for antibiotic resistance considering inherent randomness and calibrates it using advanced optimization techniques.
Developed a model for antibiotic resistance dynamics.
Employed Particle Swarm Optimization for parameter calibration.
Used k-means clustering to sample parameter space.
Estimated empirical joint distribution from selected samples.