Paper List

期刊: ArXiv Preprint
发布日期: 2026-03-11
BioinformaticsComputational Epidemiology

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

Unknown

Joyce Lee, Seth Blumberg
Figure
Figure
Figure

30秒速读

IN SHORT: This paper addresses the critical challenge of quantitatively evaluating antibiotic prescribing policies under realistic uncertainty and partial observability, where traditional observational studies are limited by incomplete data and unmeasured confounding factors.

核心创新

  • Methodology Introduces a novel 'leaky-balloon' abstraction for modeling antibiotic resistance dynamics, providing a computationally efficient yet biologically plausible representation of resistance accumulation and decay.
  • Methodology Implements a modular MDP/POMDP framework with explicit control over observability parameters (noise, bias, delay), enabling systematic study of how information degradation affects optimal prescribing strategies.
  • Methodology Provides the first Gymnasium-compatible simulation environment specifically designed for antibiotic stewardship research, bridging computational epidemiology and reinforcement learning communities.

主要结论

  • The abx_amr_simulator provides a quantitative framework for evaluating antibiotic prescribing policies, addressing the limitation that observational studies alone cannot directly quantify long-term effects of prescribing interventions.
  • The simulator's modular design enables researchers to systematically investigate how specific data deficiencies (noise, bias, delay) impede antibiotic stewardship efforts and assess potential gains from targeted interventions.
  • By balancing individual clinical outcomes (λ=0) and community resistance management (λ=1) through configurable reward functions, the framework allows exploration of trade-offs between short-term patient care and long-term public health objectives.
研究空白： Current antimicrobial resistance research lacks standardized, configurable simulation environments that can model the complex interplay between individual prescribing decisions and population-level resistance dynamics under realistic uncertainty conditions, particularly partial observability in clinical data.

摘要: 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 immediate 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.

代码