161 A System Dynamics Model of Bovine Respiratory Disease Epidemiology and Prevention Strategies in an Integrated Beef Production System

Abstract

Abstract Bovine respiratory disease (BRD) is a complex multifactorial disease that results in more than $1 billion in economic losses for the United States beef cattle industry, accounting for approximately 30% of the total antimicrobial use (AMU) in food animal production systems. The prevalence and production losses due to BRD vary significantly between different production systems, and is influenced by the host, pathogen, and environmental risk factors. These multiple risk factors together have a cumulative effect and will increase the prevalence of the disease if no preventive strategies are implemented in the system. Moreover, the increased AMU to treat BRD results in antimicrobial resistance, further complicating the issue. A linear, event-based solution to address such a complex problem will fail to consider the feedback loops and time delays in the system and may cause unintended consequences that might make the problem even more complex. Hence, we propose a more comprehensive system dynamic model to simulate an integrated beef production system with respect to BRD and predict the dynamic behavior of the system under different preventive strategies currently available to the beef industry. Our conceptual model accounts for host risk factors (auxiliary variables in the model) like age, nutritional status, daily weight gain, prior exposure to pathogens, and genetics. The environmental risk factors such as air quality, transportation, temperature humidity index, overcrowding, and source of animals were also included in the model. The various preventative strategies to reduce the incidence of BRD, including vaccination, biosecurity, adequate colostrum, and nutrition, were also added to the model. The key outcome variables considered were net profitability and AMU. The model simulates multiple scenarios by leveraging the auxiliary variables and can provide information about the system’s net profitability and total AMU. The model explains the interconnected effect of various risk factors associated with BRD in multiple complex scenarios and helps understand the combined effect of adopting various preventive strategies and host-environmental factors in controlling BRD. The initial result from our dynamic model suggests that using a combination of preventive measures and avoiding the selected host-environmental risk factors can minimize AMU while improving profit. The long-term goal of our modeling effort is to enable the beef system stakeholders to make informed decisions and strategies to mitigate the impact of BRD, thereby contributing to improved profitability. Through a systems-thinking approach, our model can contribute to addressing the challenges in BRD management currently faced by the US-based integrated beef production systems.