Ramsay Dana, McDonald Wade, Thompson Michelle, Erickson Nathan, Gow Sheryl, Osgood Nathaniel D, Waldner Cheryl
Department of Large Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK, Canada.
Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada.
Front Vet Sci. 2025 Jan 10;11:1466986. doi: 10.3389/fvets.2024.1466986. eCollection 2024.
Antimicrobial resistance (AMR) is a growing threat to the efficacy of antimicrobials in humans and animals, including those used to control bovine respiratory disease (BRD) in high-risk calves entering western Canadian feedlots. Successful mitigation strategies require an improved understanding of the epidemiology of AMR. Specifically, the relative contributions of antimicrobial use (AMU) and contagious transmission to AMR emergence in animal populations are unknown.
A stochastic, continuous-time agent-based model (ABM) was developed to explore the dynamics of population-level AMR in in pens of high-risk cattle on a typical western Canadian feedlot. The model was directly informed and parameterized with proprietary data from partner veterinary practices and AMU/AMR surveillance data where possible. Hypotheses about how AMR emerges in the feedlot environment were represented by model configurations in which detectable AMR was impacted by (1) selection arising from AMU; (2) transmission between animals in the same pen; and (3) both AMU-linked selection and transmission. Automated calibration experiments were used to estimate unknown parameters of interest for select antimicrobial classes. Calibrated parameter values were used in a series of Monte Carlo experiments to generate simulated outputs at both the and levels. Key model outputs included the prevalence of AMR by class at multiple time points across the feeding period. This study compared the relative performances of these model configurations with respect to reproducing empirical AMR data.
Across all antimicrobial classes of interest, model configurations which included the potential for contagious acquisition of AMR offered stronger fits to the empirical data. Notably, sensitivity analyses demonstrated that model outputs were more robust to changes in the assumptions underscoring AMU than to those affecting the likelihood of transmission.
This study establishes a feedlot simulation tool that can be used to explore questions related to antimicrobial stewardship in the context of BRD management. The ABM stands out for its unique hierarchical depiction of AMR in a commercial feedlot and its grounding in robust epidemiological data. Future experiments will allow for both AMU-linked selection and transmission of AMR and can accommodate parameter modifications as required.
抗菌药物耐药性(AMR)对人类和动物使用抗菌药物的疗效构成了日益严重的威胁,包括用于控制进入加拿大西部饲养场的高危犊牛的牛呼吸道疾病(BRD)的那些药物。成功的缓解策略需要更好地了解AMR的流行病学。具体而言,抗菌药物使用(AMU)和传染性传播对动物群体中AMR出现的相对贡献尚不清楚。
开发了一个基于随机连续时间代理的模型(ABM),以探索加拿大西部典型饲养场中高危牛栏内群体水平AMR的动态。该模型尽可能直接依据来自合作兽医诊所的专有数据和AMU/AMR监测数据进行构建并设置参数。关于饲养场环境中AMR如何出现的假设由模型配置表示,其中可检测到的AMR受到以下因素影响:(1)AMU引起的选择;(2)同一栏内动物之间的传播;(3)与AMU相关的选择和传播。自动校准实验用于估计选定抗菌药物类别的未知感兴趣参数。校准后的参数值用于一系列蒙特卡洛实验,以在群体和个体水平生成模拟输出。关键模型输出包括整个饲养期多个时间点各抗菌药物类别的AMR流行率。本研究比较了这些模型配置在重现经验性AMR数据方面的相对性能。
在所有感兴趣的抗菌药物类别中,包括AMR传染性获得可能性的模型配置与经验数据的拟合度更强。值得注意的是,敏感性分析表明,模型输出对强调AMU的假设变化比对影响传播可能性的假设变化更具鲁棒性。
本研究建立了一个饲养场模拟工具,可用于在BRD管理背景下探索与抗菌药物管理相关的问题。ABM因其在商业饲养场中对AMR的独特分层描述以及基于可靠的流行病学数据而脱颖而出。未来的实验将考虑与AMU相关的AMR选择和传播,并可根据需要进行参数修改。