COS 155-10 - The impacts of farm and cattle density on foot-and-mouth disease epidemic spread

Thursday, August 10, 2017: 4:40 PM
D137, Oregon Convention Center
Amanda J. Meadows, Botany and Plant Pathology, Oregon State University, Corvallis, OR, Christopher C. Mundt, Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, Matt Keeling, Mathematics Institute, University of Warwick, Coventry, United Kingdom and Michael J. Tildesley, Life Sciences, University of Warwick, Coventry, United Kingdom

Pathogen invasions pose global threats to human and animal health, biodiversity, food production, and are a significant burden on the global economy. Models can serve as a useful tool to mitigate the negative impacts of invading pathogens by providing a means to explore how different scenarios of host density, control measures, and surveillance strategies might influence epidemic severity. For highly contagious diseases affecting livestock such as foot-and-mouth disease (FMD), host density is a key factor that contributes to epidemic risk and can dictate the control measures necessary to contain the epidemic. For such diseases, there are two aspects of host density, which are seldom considered independently: the number of livestock in a unit area and the number of farms they are distributed among. We sought to determine if, and under what circumstances, one of these aspects of host density is more influential in determining epidemic impact. We approached this issue by simulating FMD outbreaks on factorial combinations of cattle and farm numbers in artificial county areas (resulting in 50 unique cattle/farm density combinations) and comparing the results with simulations on US county-level farm data. We performed sensitivity analyses on model kernel and farm-level transmissibility/susceptibility parameters to determine how sensitive the influence of farm and cattle density on epidemic impacts would be to variation these parameters.


The most conspicuous pattern emerging from both factorial and US simulations was that the county-level cattle density must be above a certain threshold value (~30 cattle/ km2) for extensive epidemics to occur, regardless of farm density. Although the important role of cattle density in modulating epidemic impact was present across the kernel, susceptibility, and transmissibility parameter values we examined, the intensity of this effect was impacted by parameter values. Nevertheless, despite variation in these parameters and county-level farm density, scenarios where cattle density was below ~30 cattle/km2 resulted far less severe epidemics. These results suggest that county-level estimates cattle and farm density could be used to gauge the potential severity of the epidemic and used to prioritize areas for surveillance and control measures. Additionally, our finding that epidemics are unlikely to occur in scenarios of high farm, but low cattle density suggests the economic and epidemic impacts of control measures aimed at reducing the number of livestock on farms in response to an outbreak could be worth considering in future studies.