Risk assessment for the design of a risk-based surveillance programme for fish farms in Switzerland (in accordance with Council Directive 2006/88/EC of the European Union).

Swiss aquaculture farms were assessed according to their risk of acquiring or spreading viral haemorrhagic septicaemia (VHS) and infectious haematopoietic necrosis (IHN). Risk factors for the introduction and spread of VHS and IHN were defined and assessed using published data and expert opinions. Among the 357 aquaculture farms identified in Switzerland, 49.3% were categorised as high risk, 49.0% as medium risk and 1.7% as low risk. According to the new Directive 2006/88/EC for aquaculture of the European Union, the frequency of farm inspections must be derived from their risk levels. A sensitivity analysis showed that water supply and fish movements were highly influential on the output of the risk assessment regarding the introduction of VHS and IHN. Fish movements were also highly influential on the risk assessment output regarding the spread of these diseases

Demographic model of the Swiss cattle population for the years 2009-2011 stratified by gender, age and production type

Demographic composition and dynamics of animal and human populations are important determinants for the transmission dynamics of infectious disease and for the effect of infectious disease or environmental disasters on productivity. In many circumstances, demographic data are not available or of poor quality. Since 1999 Switzerland has been recording cattle movements, births, deaths and slaughter in an animal movement database (AMD). The data present in the AMD offers the opportunity for analysing and understanding the dynamic of the Swiss cattle population. A dynamic population model can serve as a building block for future disease transmission models and help policy makers in developing strategies regarding animal health, animal welfare, livestock management and productivity. The Swiss cattle population was therefore modelled using a system of ordinary differential equations. The model was stratified by production type (dairy or beef), age and gender (male and female calves: 0-1 year, heifers and young bulls: 1-2 years, cows and bulls: older than 2 years). The simulation of the Swiss cattle population reflects the observed pattern accurately. Parameters were optimized on the basis of the goodness-of-fit (using the Powell algorithm). The fitted rates were compared with calculated rates from the AMD and differed only marginally. This gives confidence in the fitted rates of parameters that are not directly deductible from the AMD (e.g. the proportion of calves that are moved from the dairy system to fattening plants)

Cost and sensitivity of on-farm versus slaughterhouse surveys for prevalence estimation and substantiating freedom from disease

Within the framework of Swiss surveillance for epizootic diseases, dairy cattle are sampled using bulk tank milk while non-dairy cattle are sampled on the farm. The latter method is costly, time-demanding and dangerous for the personnel. However, slaughterhouses could be an alternative sampling point for this population. To assess the cost-effectiveness and sensitivity of such an approach, surveillance using slaughterhouse sampling was modelled with data from the 2012 Swiss animal movement database (AMD). We simulated a cross-sectional study for bluetongue (BT), and surveillance programmes to substantiate freedom from infectious bovine rhinotracheitis (IBR) and enzootic bovine leucosis (EBL) (combined) to compare the outcome of random on-farm sampling versus slaughterhouse sampling. We found that, under Swiss conditions, slaughterhouse sampling results in low herd-level sensitivities because animals are sent by owners to slaughter individually and not in large groups, restricting the number of samples per herd. This makes slaughterhouse sampling inappropriate for prevalence surveys at the herd-level. However, for prevalence surveys at the animal-level and for substantiation of freedom from disease, slaughterhouse surveillance is equally or more cost-efficient than on-farm sampling

Evaluation of bovine viral diarrhoea virus control strategies in dairy herds in Hokkaido, Japan, using stochastic modelling

Bovine viral diarrhoea virus (BVDV) infection in cattle can result in growth retardation, reduced milk production, reproductive disorders and death. Persistently infected animals are the primary source of infection. In Hokkaido, Japan, all cattle entering shared pastures in summer are vaccinated before movement for disease control. Additionally, these cattle may be tested for BVDV and culled if positive. However, the effectiveness of this control strategy aiming to reduce the number of BVDV-infected animals has not been assessed. The aim of this study was to evaluate the effectiveness of various test-and-cull and/or vaccination strategies on BVDV control in dairy farms in two districts of Hokkaido, Nemuro and Hiyama. A stochastic model was developed to compare the different control strategies over a 10-year period. The model was individual-based and simulated disease dynamics both within and between herds. Parameters included in the model were obtained from the literature, the Hokkaido government and the Japanese Ministry of Agriculture, Forestry and Fisheries. Nine different scenarios were compared as follows: no control, test-and-cull strategies based on antigen testing of either calves or only cattle entering common pastures, vaccination of all adult cattle or only cattle entering shared pastures and combinations thereof. The results indicate that current strategies for BVDV control in Hokkaido slightly reduced the number of BVDV-infected animals; however, alternative strategies such as testing all calves and culling any positives or vaccinating all susceptible adult animals dramatically reduced those. To our knowledge, this is the first report regarding the comparison of the effectiveness between the current strategies in Hokkaido and the alternative strategies for BVDV control measures

Nomenclature for subscripts in Equations 1–12.

<p>Nomenclature for subscripts in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0109329#pone.0109329.e002" target="_blank">Equations 1</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0109329#pone.0109329.e013" target="_blank">12</a>.</p

Seasonal pattern of birth and mortality in calves.

<p>Solid line: model data, dashed lines: AMD data. Orange: dairy male calf, red: dairy female calf, blue: beef male calf, green: beef female calf.</p

Slaughter numbers per age category.

<p>a) Dairy population. b) Beef population. Solid line: model data, dashed lines: AMD data. Light blue: cow, orange: male calf, red: female calf, pink: heifer, blue: young bull, purple: bull.</p

Monthly population parameters for the Swiss cattle population.

<p>D: dairy; B: beef; F: female; M: male; X: calf, Y: subadult, Z: adult. Small letters indicate rates (s: slaughter, m: mortality, f: fattening, tr: transition to next age class). μ1: average birth rate; μ2: average mortality rate;</p><p>Monthly population parameters for the Swiss cattle population.</p

Compartments and parameters in Equations 1–12.

<p>Compartments and parameters in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0109329#pone.0109329.e002" target="_blank">Equations 1</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0109329#pone.0109329.e013" target="_blank">12</a>.</p