District-Wide Herd Sanitation and Eradication of Intramammary Staphylococcus aureus Genotype B Infection in Dairy Herds in Ticino, Switzerland.

The present study demonstrates successful herd sanitation and eradication of contagious mastitis caused by Staphylococcus aureus genotype B (S. aureus GTB) in an entire Swiss district (Ticino) including 3,364 dairy cows from 168 farms. Herd sanitation included testing of all cows using a highly GTB specific and sensitive qPCR assay, implementation of related on-farm measures, appropriate antibiotic therapy of GTB-positive cows and culling of therapy-resistant animals, respectively. A treatment index was used as an objective criterion to select GTB-positive cows eligible for culling and replacement payment. 62 herds (37%) were initially GTB-positive with a cow prevalence between 10% and 100% and were submitted to sanitation. Twenty mo after the start of the campaign, all these herds were free from S. aureus GTB, whereby 73% of them were sanitized during the first 7 mo. At the cow level, a total of 343 animals were infected. 50 of them were immediately culled and financially compensated based on their treatment index value. The remaining 293 cows were intramammarily treated with antibiotics either during lactation using the combination of cephalexin-kanamycin or penicillin-gentamicin or at dry-off using cloxacillin. Out of these cows, 275 (93.9%) were treated successfully meaning that their milk was twice GTB-negative by qPCR after therapy. For lactational treatment, control samples were taken ≥10 and ≥20 d after treatment, for dry off treatment ≥14 and ≥24 d after parturition. Neither lactation number nor SCC before treatment of the cow nor the type of therapy were associated with therapeutic cure. Using data of 30 GTB-positive and 71 GTB-negative herds (1855 observations), the impact of GTB sanitation on bulk tank milk SCC (BTSCC) was evaluated applying a linear mixed statistical model. In the year before sanitation, BTSCC was always higher in GTB positive than in GTB negative herds. After the start of the campaign, BTSCC declined rapidly in the herds under GTB sanitation and achieved values that no longer differed statistically from those of GTB-free herds after only 2 mo, remaining very similar for the rest of the campaign. The farmers were very satisfied with the outcome of the campaign as all GTB positive herds could be sanitized rapidly, sanitation was sustainable, and milk quality increased.

Experiences with a voluntary surveillance system for early detection of equine diseases in Switzerland.

Clinical observations made by practitioners and reported using web- and mobile-based technologies may benefit disease surveillance by improving the timeliness of outbreak detection. Equinella is a voluntary electronic reporting and information system established for the early detection of infectious equine diseases in Switzerland. Sentinel veterinary practitioners have been able to report cases of non-notifiable diseases and clinical symptoms to an internet-based platform since November 2013. Telephone interviews were carried out during the first year to understand the motivating and constraining factors affecting voluntary reporting and the use of mobile devices in a sentinel network. We found that non-monetary incentives attract sentinel practitioners; however, insufficient understanding of the reporting system and of its relevance, as well as concerns over the electronic dissemination of health data were identified as potential challenges to sustainable reporting. Many practitioners are not yet aware of the advantages of mobile-based surveillance and may require some time to become accustomed to novel reporting methods. Finally, our study highlights the need for continued information feedback loops within voluntary sentinel networks.

Value of evidence from syndromic surveillance with cumulative evidence from multiple data streams with delayed reporting.

Delayed reporting of health data may hamper the early detection of infectious diseases in surveillance systems. Furthermore, combining multiple data streams, e.g. aiming at improving a system's sensitivity, can be challenging. In this study, we used a Bayesian framework where the result is presented as the value of evidence, i.e. the likelihood ratio for the evidence under outbreak versus baseline conditions. Based on a historical data set of routinely collected cattle mortality events, we evaluated outbreak detection performance (sensitivity, time to detection, in-control run length) under the Bayesian approach among three scenarios: presence of delayed data reporting, but not accounting for it; presence of delayed data reporting accounted for; and absence of delayed data reporting (i.e. an ideal system). Performance on larger and smaller outbreaks was compared with a classical approach, considering syndromes separately or combined. We found that the Bayesian approach performed better than the classical approach, especially for the smaller outbreaks. Furthermore, the Bayesian approach performed similarly well in the scenario where delayed reporting was accounted for to the scenario where it was absent. We argue that the value of evidence framework may be suitable for surveillance systems with multiple syndromes and delayed reporting of data.