The potential of antimicrobial resistance diagnostics to inform prudent antimicrobial use in feedlot cattle: dynamic models as tools for optimizing interventions in bovine respiratory disease

Antimicrobials are used in modern livestock production systems to control and treat bacterial diseases in food animals. The misuse and overuse of antimicrobial drugs in agricultural settings as elsewhere accelerates the selection of resistant pathogens; emerging antimicrobial resistance (AMR) threatens the therapeutic efficacy of available antimicrobials, leading to treatment failures, production losses and food insecurity. Bovine respiratory disease (BRD) is the primary reason for injectable antimicrobial use (AMU) in Canadian feedlots. Global authorities recommend that diagnostic tests should be used to guide therapeutic drug selection in food animals to reduce unnecessary AMU and slow AMR. However, the potential for laboratory testing to inform AMU and favourably impact BRD and AMR outcomes at the population level has not been fully explored. The problem of AMR in BRD management demands a novel approach that recognizes the complexity of food animal systems. There is growing interest in the use of dynamic models to explore hypotheses about the relationships between AMU and AMR in animal populations. Dynamic models are mathematical representations of complex, time-varying systems and have been used to optimize intervention strategies in other food production contexts. This research explores the hypotheses that agent-based models (ABMs) are similarly useful tools for 1) investigating the dynamics of population-level AMR in BRD pathogens; and 2) experimenting with AMR testing interventions proposed to advance antimicrobial stewardship goals in feedlots. Central to this work was the development, parameterization and calibration of a feedlot simulation tool (i.e., a stochastic, continuous-time ABM) with reference to best practice guidelines. The thesis herein is structured around five key objectives, namely: 1) to describe how dynamic models have been used to investigate the AMU/AMR relationship; 2) to ground the ABM in robust epidemiological data; 3) to explicitly document the model’s assumptions and data sources; 4) to evaluate hypotheses concerning AMR emergence in western Canadian feedlots; and 5) to assess the possibility for pen-level diagnostic testing to inform BRD treatments. In pursuing these deliverables, I highlight the complex relationships between factors affecting the emergence of resistance in BRD pathogens, including strategies intended to limit the risks associated with AMR. Further, this work fully engages with and advances what is known about using a systems science approach to evaluate the impacts of AMU and related interventions on AMR in production animals.

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