-Surveillance for rare and emerging infectious diseases poses a special challenge to veterinary services. Most emerging infectious diseases like bovine tuberculosis (bTB) are zoonoses, affecting both human and animal populations. Despite the low prevalence of such an emerging infectious disease at time of incursion, the surveillance system should be able to detect the presence of the disease as early as possible. Because passive surveillance is a relatively cost-effective and therefore commonly used process, it is the basic tool for infectious disease surveillance. Because of under-reporting in passive surveillance, cost-intensive active surveillance is often required to increase the sensitivity of the surveillance system. Using scenario tree modelling, the sensitivity of passive and active surveillance system components (SSC) can be quantified and an optimal, cost-effective surveillance system developed considering the contributions of each SSC. We illustrate this approach with the example of bTB surveillance in Switzerland where the surveillance system for bTB consists of meat inspection at the slaughterhouse (SLI), passive clinical surveillance on farm (CLIN) and human surveillance (HS). While the sensitivities for CLIN and HS were both negligible (<1%), SLI was assessed to be 55.6%. The scenario tree model showed that SLI is increasable up to 80.4% when the disease awareness of meat inspectors in Switzerland is enhanced. A hypothetical random survey (RS) was also compared with a targeted survey (TS) in high-risk strata of the cattle population, and the sensitivity of TS was 1.17-fold better than in RS but with 50% of the sample size.scenario tree modelling / surveillance system / emerging infectious disease / bovine tuberculosis / Switzerland
In order to estimate the diversity, clinical involvement and zoonotic potential of parasites in pigs submitted for diagnosis to the PathoPig project of the Swiss Federal Food Safety and Veterinary Office, faeces (n=125) from suckling piglets (n=39), weaners (n=60) and piglets beginning fattening (n=26) from 74 Swiss farms were examined by 3 coproscopical methods (i.e. sedimentation/zinc chloride-flotation; SAFC and Ziehl-Neelsen staining). Samples microscopically positive for Cryptosporidium were further tested by PCR/sequencing for species assessment. The most frequently detected parasite was Balantidium coli, a facultative pathogenic ciliate with zoonotic potential, in 5.1, 36.7 and 50.0% of suckling, weaners and fatteners and 43.2% of farms; however, no association with disease was observed. Isospora (syn. Cystoisospora) suis infections were detected in 13.3 and 11.1% of suckling piglets with and without diarrhoea, and in 10.0 and 13.3% of weaners and fatteners with diarrhoea, respectively, and were significant associated with emaciation. Cryptosporidium infections were detected in 10.3, 15.0 and 19.2% of sucklings, weaners and fatteners, respectively, and in 18.9% of the farms. Interestingly, two age-related species were identified: C. suis in younger piglets (2 to 6weeks) and C. scrofarum in older ones (6 to 17weeks). None of the pigs infected with C. scrofarum (n=8), but 3 of 4 piglets infected with C. suis (co-infection with I. suis in 2 cases) had diarrhoea. The zoonotic species C. parvum was not detected, nevertheless, sporadic cases of human infection with the porcine-adapted species have been reported. Ascaris suum, Trichuris suis and Strongylida were rarely detected (<4%) in all age categories.
Vector-borne diseases pose a special challenge to veterinary authorities due to complex and time-consuming surveillance programs taking into account vector habitat. Using stochastic scenario tree modelling, each possible surveillance activity of a future surveillance system can be evaluated with regard to its sensitivity and the expected cost. The overall sensitivity of various potential surveillance systems, composed of different combinations of surveillance activities, is calculated and the proposed surveillance system is optimized with respect to the considered surveillance activities, the sensitivity and the cost. The objective of this project was to use stochastic scenario tree modelling in combination with a simple cost analysis in order to develop the national surveillance system for Bluetongue in Switzerland. This surveillance system was established due to the emerging outbreak of Bluetongue virus serotype 8 (BTV-8) in Northern Europe in 2006. Based on the modelling results, it was decided to implement an improved passive clinical surveillance in cattle and sheep through campaigns in order to increase disease awareness alongside a targeted bulk milk testing strategy in 200 dairy cattle herds located in high-risk areas. The estimated median probability of detection of cases (i.e. sensitivity) of the surveillance system in this combined approach was 96.4%. The evaluation of the prospective national surveillance system predicted that passive clinical surveillance in cattle would provide the highest probability to detect BTV-8 infected animals, followed by passive clinical surveillance in sheep and bulk milk testing of 200 dairy cattle farms in high-risk areas. This approach is also applicable in other countries and to other epidemic diseases.
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