abx_amr_simulator: A simulation environment for antibiotic prescribing policy optimization under antimicrobial resistance

TL;DR

abx_amr_simulator is a flexible simulation environment for optimizing antibiotic prescribing policies using reinforcement learning, modeling antimicrobial resistance dynamics and decision-making under uncertainty.

Contribution

It introduces a modular, RL-compatible simulation platform for studying antibiotic stewardship and resistance management with customizable scenarios.

Findings

Provides a configurable benchmark for RL in healthcare.

Models AMR dynamics with a leaky-balloon abstraction.

Supports partial observability scenarios with noise and delays.

Abstract

Antimicrobial resistance (AMR) poses a global health threat, reducing the effectiveness of antibiotics and complicating clinical decision-making. To address this challenge, we introduce abx_amr_simulator, a Python-based simulation package designed to model antibiotic prescribing and AMR dynamics within a controlled, reinforcement learning (RL)-compatible environment. The simulator allows users to specify patient populations, antibiotic-specific AMR response curves, and reward functions that balance immedi- ate clinical benefit against long-term resistance management. Key features include a modular design for configuring patient attributes, antibiotic resistance dynamics modeled via a leaky-balloon abstraction, and tools to explore partial observability through noise, bias, and delay in observations. The package is compatible with the Gymnasium RL API, enabling users to train and test RL agents under diverse clinical scenarios. From an ML perspective, the package provides a configurable benchmark environment for sequential decision-making under uncertainty, including partial observability induced by noisy, biased, and delayed observations. By providing a customizable and extensible framework, abx_amr_simulator offers a valuable tool for studying AMR dynamics and optimizing antibiotic stewardship strategies under realistic uncertainty.