Levin B R
Emory University, Department of Biology, Atlanta, GA 30322, USA.
Neth J Med. 2002 Aug;60(7 Suppl):58-64; discussion 64-6.
I consider three mathematical models of the epidemiology of antibiotic treatment and the evolution of resistance. All of these models explore the relationship between the volume of antibiotic use and the frequency and rate of ascent (or descent) of resistance. The first model is in the population genetics tradition and assumes that in the absence of treatment the frequency of resistance wanes at a rate proportional to the fitness costs associated with resistance, but precipitously ascends to high frequencies in treated patients. The second two models are in the compartment, or SIR, model tradition of infectious disease epidemiology. The first of these considers the relationship between resistance and rates of antibiotic treatment in open communities. The second explores the factors contributing to the frequency of resistance in the closed settings of hospitals and nursing homes. While I give some consideration to the epidemiological and medical implications of the results of the analysis of the properties of these models, for the most part the models are the message. I end with a harangue about the utility of simple mathematics for these considerations and a plea to obtain realistic estimates of the parameters of these models and test the validity of the predictions generated from the analysis of these models.
我考虑了抗生素治疗流行病学和耐药性演变的三种数学模型。所有这些模型都探讨了抗生素使用量与耐药性上升（或下降）的频率和速率之间的关系。第一个模型遵循群体遗传学传统，假设在不进行治疗的情况下，耐药性频率以与耐药相关的适应度成本成比例的速率下降，但在接受治疗的患者中会急剧上升至高频。后两个模型遵循传染病流行病学的 compartment 模型或 SIR 模型传统。其中第一个考虑了开放社区中耐药性与抗生素治疗速率之间的关系。第二个探讨了导致医院和疗养院等封闭环境中耐药性频率的因素。虽然我对这些模型特性分析结果的流行病学和医学意义进行了一些思考，但在很大程度上，模型本身就是关键所在。我最后慷慨陈词，强调简单数学对于这些思考的实用性，并呼吁获取这些模型参数的现实估计值，并检验从这些模型分析中得出的预测的有效性。
