A linear programming approach to estimate the economic impact of bovine viral diarrhoea (BVD) at the whole-farm level in Scotland

We combined epidemiological and economic concepts and modelling techniques, to integrate animal health into whole-farm business management. This allowed us to assess the relative contribution that disease prevention could make to whole-farm income and to the variability in farm income (risk). It also allowed us to assess disease losses in the context of a farm business rather than as a disease outbreak in isolation. A linear program ("MOTAD") establishes the combination of decision maker's activities that minimise risk for a given level of income within farm-business constraints. The MOTAD model was applied to farm-management decision making in Scottish cow-calf herds and was linked to an epidemiological model of bovine viral diarrhoea (BVD). When BVD was considered in isolation (i.e. without taking into account risk), the minimum expected total cost of BVD (sum of output losses plus expenditure on prevention) was similar whether the herd was susceptible to BVD or of unknown BVD-status at the outset. However, the expected total cost of BVD fell in response to increasing expenditure on prevention in 'susceptible' herds. This relationship was not apparent in herds of unknown BVD-status. As a consequence of this difference, 'susceptible' herds were better able to use investment in BVD biosecurity as a means to increase farm income at minimum risk than herds of unknown BVD-status. 'Susceptible' herds therefore were able to achieve high income targets with less-intensive production than herds of unknown BVD-status. This suggested that maintaining a cow-calf herd free of BVD contributes to farm income and risk management indirectly through its effect on the management of the whole farm. It follows that measurement of the economic impact of BVD requires a whole-farm perspective that includes a consideration of risk. Because farmers generally are considered to be risk adverse, this means that the least-cost disease-control option might not always be the preferred option.     

A practical approach to calculate sample size for herd prevalence surveys

When designing a herd-level prevalence study that will use an imperfect diagnostic test, it is necessary to consider the test sensitivity and specificity. A new approach was developed to take into account the imperfections of the test. We present an adapted formula that, when combined with an existing piece of software, allows improved planning. Bovine paratuberculosis is included as an example infection because it originally stimulated the work. Examples are provided of the trade-off between the benefit (low number of herds) and the disadvantage (large number of animals per herd and exclusion of small herds) that are associated with achieving high herd-level sensitivity and specificity. We demonstrate the bias in the estimate of prevalence and the underestimate of the confidence range that would arise if we did not account for test sensitivity and specificity.