Aujeszky's disease (AD) is a respiratory, infectious viral illness associated with high mortality, especially in neonatal piglets and has frequently been considered an economically important disease in many endemic countries. Although AD is still occurring in a geographically defined region in Korea, little attention has been paid to the economics of AD. In this study, partial budget technique was used to develop a simulation model to measure financial losses following the disease epidemic in a swine operation utilizing stochastic or deterministic parameters from the literatures and the index case herd of AD occurred in 2005, where available and applicable. For the infected case herd with a 12500-pig, the total economic loss for this operation was estimated to be about 199 million Korean won (95% confidence interval [CI] 148,645,000-250,741,000). Given net loss due to death of a pig at sow level was 119,000 won, total loss for the case herd with 1200 sows accounted for 143 million won (95% CI 92,599,000-193,729,000). The net loss of the death of one pig at growing and fattening level resulted in loss of 46,000 won (95% CI 40,000-53,000) and 126,000 won (95% CI 122,000-131,000), respectively. Taking into account for the number of pigs raised in the case herd, total loss amounted to 8 million won (95% CI 7,167,000-9,347,000) and 12 million won (95% CI 11,959,000-12,891,000), for growers and fatteners, respectively, assuming 63% of saved feed intake when a pig dies halfway through the respective period. Under the model's assumptions, suckling pig mortality was the major factors of loss in estimating the economic consequences (approximately 71.8% of the total loss). The high economic losses of a herd infected with AD suggest that the effective and region-specific control measures should be implemented in disease endemic foci.

The objective of this study was to analyze data from the planned national serological monitoring program for Aujeszky's disease (AD) using a simulation model to evaluate probable outcomes expected in the sample derived from the simulated herds at predefined within-herd prevalence and herd prevalence. Additionally, prevalence at animal- and herd-level estimated by the stochastic simulation model based on the distributions of the proportion of infected herds and test-positive animals was compared with those of data from a national serological survey in 2006, in which 106,762 fattening pigs from 5,325 herds were tested for AD using a commercial ELISA kit. A fixed value of 95% was used for test sensitivity, and the specificity was modeled with a minimum, most likely and maximum of 95, 97 and 99%, respectively. The within-herd prevalence and herd prevalence was modeled using Pert and Triang distributions, respectively with a minimum, most likely and maximum point values. In all calulations, population size of 1,000 was used due to lack of representative information. The mean number of infected herds and true test-positives was estimated to be 27 herds (median=25; 95% percentile 44) and 214 pigs (median=196; 95% percentile 423), respectively. When testing 20 pigs (mean of 2006 survey) in each herd, there was a 3.3% probability that the potential for false-positive reactions due to less than 100% specificity of the ELISA test would be detected. It was found that the model showed prevalence of 0.21% (99% percentile 0.50%) and 0.5% (99% percentile 0.99%) at animal- and herd-level, respectively. These rates were much similar to data from the 2006 survey (0.62% versus 0.83%). The overall mean herd-level sensitivity of the 2006 survey for fattening pigs was 99.9%, with only a 0.2% probability of failing to detect at least one infected herd.