Abstract
Endemic diseases of cattle, such as bovine viral diarrhea, have significant impact on production efficiency of food of animal origin with consequences for animal welfare and climate change reduction targets. Many modeling studies focus on the local scale, examining the on-farm dynamics of this infectious disease. However, insight into prevalence and control across a network of farms ultimately requires a network level approach. Here, we implement understanding of infection dynamics, gained through these detailed on-farm modeling studies, to produce a national scale model of bovine viral diarrhea virus transmission. The complex disease epidemiology and on-farm dynamics are approximated using SIS dynamics with each farm treated as a single unit. Using a top down approach, we estimate on-farm parameters associated with contraction and subsequent clearance from infection at herd level. We examine possible control strategies associated with animal movements between farms and find measures targeted at a small number of high-movement farms efficient for rapid and sustained prevalence reduction.
Highlights
Food production for human consumption needs to become more efficient in order to respond to increasing demand for land usage and to aid in the meeting of climate change targets [1]
A selection of simulation results produced during the model calibration stage are shown in Figure 2 along with the observed values measured during the Scottish bovine viral diarrhea (BVD) prevalence study
The times taken for the herd prevalence of active bovine viral diarrhea virus (BVDV) infection to drop below 2% across the network are shown in Figure 4a and 4b for control strategies 1 and 2
Summary
Food production for human consumption needs to become more efficient in order to respond to increasing demand for land usage and to aid in the meeting of climate change targets [1] Endemic pathogens, such as bovine herpesvirus 1, parainfluenza 3 virus and bovine viral diarrhea virus (BVDV), have both a detrimental impact on this efficiency as well as animal welfare issues. Sustainable reduction of endemic diseases requires control measures applied across a network of farms for those diseases where disease transmission is primarily associated with the movement of infected animals These factors, along with the complexity of modeling on-farm epidemiology, suggest a top down approach to assess the impact of control measures targeting animal movement on disease burden for an endemic, economically relevant infectious disease. Our model incorporates only key dynamics of the disease, reducing parameter estimation, and uses available data for movement and calibration where available
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