Rosato Conor, Green Peter L, Harris John, Maskell Simon, Hope William, Gerada Alessandro, Howard Alex
Department of Pharmacology and Therapeutics, University of Liverpool, L69 7BE Liverpool, U.K.
Department of Mechanical Engineering, University of Liverpool, L69 7BE Liverpool, U.K.
IEEE Access. 2024;12:100772-100791. doi: 10.1109/ACCESS.2024.3427410.
Antimicrobial resistance (AMR) emerges when disease-causing microorganisms develop the ability to withstand the effects of antimicrobial therapy. This phenomenon is often fueled by the human-to-human transmission of pathogens and the overuse of antibiotics. Over the past 50 years, increased computational power has facilitated the application of Bayesian inference algorithms. In this comprehensive review, the basic theory of Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) methods are explained. These inference algorithms are instrumental in calibrating complex statistical models to the vast amounts of AMR-related data. Popular statistical models include hierarchical and mixture models as well as discrete and stochastic epidemiological compartmental and agent based models. Studies encompassed multi-drug resistance, economic implications of vaccines, and modeling AMR in vitro as well as within specific populations. We describe how combining these topics in a coherent framework can result in an effective antimicrobial stewardship. We also outline recent advancements in the methodology of Bayesian inference algorithms and provide insights into their prospective applicability for modeling AMR in the future.
当致病微生物产生耐受抗菌治疗效果的能力时，就会出现抗菌药物耐药性（AMR）。这种现象通常由病原体在人与人之间的传播以及抗生素的过度使用所推动。在过去50年中，计算能力的提高促进了贝叶斯推理算法的应用。在这篇全面综述中，解释了马尔可夫链蒙特卡罗（MCMC）和序贯蒙特卡罗（SMC）方法的基本理论。这些推理算法有助于将复杂的统计模型校准到大量与AMR相关的数据。流行的统计模型包括分层模型和混合模型，以及离散和随机的流行病学分区模型和基于主体的模型。研究涵盖了多重耐药性、疫苗的经济影响，以及在体外和特定人群中对AMR进行建模。我们描述了如何在一个连贯的框架中结合这些主题，以实现有效的抗菌药物管理。我们还概述了贝叶斯推理算法方法的最新进展，并深入探讨了它们未来在AMR建模中的潜在适用性。
