Footrot is a wide-spread and debilitating infectious disease of sheep caused by Dichelobacter nodosus that has a major impact on the UK sheep industry. It causes pain and lesions on the feet that result in lameness. Footrot reduces animal welfare and impacts on farm productivity and the sustainability of sheep farming with annual losses to the UK sheep industry of about £80 million. Footrot increases the environmental impact of sheep farming, reducing UK competitiveness and adversely affects national and international food security.
This project will determine strain, serogroup and virulence profiles of D. nodosus communities in unaffected and affected sheep and flocks using studies focused on periods of change in the environment or management of footrot. These results will be used to develop statistical and mathematical models that will fundamentally alter our understanding of this disease. Our ultimate aim is to identify flock specific control programmes informed by mathematical models of the molecular epidemiology of D. nodosus.
1. Understand the role of the presence and abundance of D. nodosus and its virulence factors in flocks in temperate climates with constant presence and almost constant disease spread.
2. Use mathematical modeling to simulate the colonisation of feet, disease triggers and transmission between sheep in a range of situations and climates.
3. To test flock control strategies to evaluate the most successful at controlling footrot disease and transmission.
In order to address the project aims, existing and new data are being used from three types of epidemiological study.
1. Samples from ~10 flocks without footrot.
2. Samples and data on disease from two longitudinal studies of flocks with changing incidence of footrot over time with change in climate.
3. A clinical trial of 24 flocks where flock specific managements to minimise footrot are being tested.
From these studies, laboratory data from swabs from interdigital skin of healthy and diseased sheep are being tested for presence and load of D. nodosus DNA, serogroup and virulence. In addition, epidemiological data are being stored.
The laboratory and epidemiological data from the studies will be used to develop a suite of mixed effect multistate models to investigate the associations between presence, abundance, virulence factors and serogroup profile of D. nodosus on the likelihood of transitions between different states (healthy, ID, FR) of feet, sheep and flocks.
In years 1-1.5, existing data will be used to develop mathematical models of increasing complexity from foot to flock. These will be used to test our current understanding of the role of D. nodosus load and virulence at the foot, sheep and flock levels.
Dynamics at the foot scale:
Will infer the interactions between different strains of D. nodosus and the disease status of the host. We will examine models of D. nodosus community using serogroups as markers for strains to understand the changes that may lead to footrot.
Dynamics at the animal scale:
We will quantify the transmission of infection between sheep and feet. At the scale of the individual animal, we know that correlations in disease and load exist between the status of each foot. Our aim is to develop a model that captures the dynamic transitions of each hoof between the three states of disease (healthy, Interdigital Dermatitis (ID) and Severe Footrot (SFR)). Given 3 states for each foot and 4 feet per animal there are a total of 81 states that an individual animal can be in. Here, the goal will be to infer parameters that describe the natural transmission between disease states based on the results of field and laboratory studies.
Dynamics at the flock scale:
These models will aim to understand the causes of disease based on the composition and quantity of D. nodosus strains on the foot. Our goal is to generate a mathematical description of the dynamics of footrot in a flock capturing the known behaviour at smaller scales and the interaction between sheep, the pathogens and the environment. A flexible approach will be to utilize a stochastic individual-based model, where the behaviour and history of each animal can be studied and manipulated in detail. As above, we will classify each animal into one of 81 states (dependent on the status of each foot), but we will explicitly model the interaction between sheep and the environment. For such individual based models we express the dynamics in terms of rates. This model will be used to test our overarching hypothesis that disease is linked to both the abundance of D. nodosus on the farm and the virulence factors present; but that there is necessarily a complex feedback between disease, abundance and virulence.
From year 1.5-3 the above models will be used alongside new data from studies 1-3 to simulate the likely impact of targeted flock control measures and specific vaccines and their likely effect on foot state. We anticipate from current managements that several managements will successfully drive down D. nodosus load and virulence factors and we will be able to identify this from the modelling.