The Comparison of Three Statistical Models for Syndromic Surveillance in Cattle Using Milk Production Data.

Two vector-borne infections have emerged and spread throughout the north-western part of Europe in the last decade: Bluetongue virus serotype-8 (BTV-8) and the Schmallenberg virus (SBV). The objective of the current study was to compare three statistical methods when applied in a syndromic surveillance context for the early detection of emerging diseases in cattle in the Netherlands. Since BTV-8 and SBV both have a negative effect on milk production in dairy cattle, routinely collected bulk milk recordings were used to compare the three statistical methods in their potential to detect drops in milk production during a period of seven years in which BTV-8 and SBV emerged. A Cusum algorithm, Bayesian disease mapping model, and spatiotemporal cluster analysis using the space-time scan statistic were performed and their performance in terms of sensitivity and specificity was compared. Spatiotemporal cluster analysis performed best for early detection of SBV in cattle in the Netherlands with a relative sensitivity of 71% compared to clinical surveillance and 100% specificity in a year without major disease outbreaks. Sensitivity to detect BTV-8 was low for all methods. However, many alerts of reduced milk production were generated several weeks before the week in which first clinical suspicions were reported. It cannot be excluded that these alerts represent the actual first signs of BTV-8 infections in cattle in the Netherlands thus leading to an underestimation of the sensitivity of the syndromic surveillance methods relative to the clinical surveillance in place.

Treatment of clinical Brachyspira hyodysenteriae with zinc chelate in pigs: a blinded, randomised controlled trial.

BACKGROUND: Brachyspira hyodysenteriae infection in pigs ('swine dysentery') leads to decreased feed conversion, growth losses and mortality. Current countermeasures have the downside of antibiotic resistance (antibiotics) and ecotoxicity (zinc oxide). The aim of this study was to evaluate the effect of a novel zinc chelate (Intra Dysovinol (ID)) on clinical signs of swine dysentery and shedding of B hyodysenteriae under field conditions. METHODS: In a randomised, double-blinded, controlled trial under Good Clinical Practice on two commercial farms, 58 B hyodysenteriae positive pigs from 16 pens received drinking water containing ID, or placebo, during six consecutive days. Faecal quality (consistency, colour, additions) was scored and faeces were analysed for presence of B hyodysenteriae by PCR. ID treatment positively affected faecal quality (consistency) and daily growth rates. RESULTS: At the last treatment day, B hyodysenteriae was not detectable in the faeces of any of the ID-treated animals, while all placebo animals remained B hyodysenteriae positive by PCR. All ID-treated animals recovered, while 5 placebo-treated animals died and 12 placebo pigs required additional treatment before the end of the study (up to 14 days after treatment start). CONCLUSION: This non-antibiotic treatment stopped the clinical signs and shedding of B hyodysenteriae in naturally infected pigs.

Application of syndromic surveillance on routinely collected cattle reproduction and milk production data for the early detection of outbreaks of Bluetongue and Schmallenberg viruses.

This study aimed to evaluate the use of routinely collected reproductive and milk production data for the early detection of emerging vector-borne diseases in cattle in the Netherlands and the Flanders region of Belgium (i.e., the northern part of Belgium). Prospective space-time cluster analyses on residuals from a model on milk production were carried out to detect clusters of reduced milk yield. A CUSUM algorithm was used to detect temporal aberrations in model residuals of reproductive performance models on two indicators of gestation length. The Bluetongue serotype-8 (BTV-8) epidemics of 2006 and 2007 and the Schmallenberg virus (SBV) epidemic of 2011 were used as case studies to evaluate the sensitivity and timeliness of these methods. The methods investigated in this study did not result in a more timely detection of BTV-8 and SBV in the Netherlands and BTV-8 in Belgium given the surveillance systems in place when these viruses emerged. This could be due to (i) the large geographical units used in the analyses (country, region and province level), and (ii) the high level of sensitivity of the surveillance systems in place when these viruses emerged. Nevertheless, it might be worthwhile to use a syndromic surveillance system based on non-specific animal health data in real-time alongside regular surveillance, to increase the sense of urgency and to provide valuable quantitative information for decision makers in the initial phase of an emerging disease outbreak.

Use of monthly collected milk yields for the detection of the emergence of the 2007 French BTV epizootic.

Two culicoides-borne diseases, Bluetongue (BTV) and Schmallenberg, have emerged in the European cattle population since 2006. Other diseases transmitted by these vectors could emerge. This justifies the development of syndromic surveillance programs whereby one or several indicators would be routinely monitored for the early detection of emerging diseases. The aim of this study was to evaluate milk yield from milk recording in dairy cattle as an indicator to be included in an emerging disease surveillance system. It was hypothesized that emergences would result in episodes of low milk production clustered in space and time. The 2007 BTV epizootic in France was used as a case study. Because it had already emerged in neighbouring countries, the disease emergence was expected and notification was mandatory. Herd-test-day milk productions were predicted for the entire country for 2006 and 2007 from herd historical data using linear mixed models. The differences between observed and predicted milk productions were averaged per week and per municipality and used as input for a space-time prospective scan statistic. Log likelihood ratios (LLR) associated with clusters were used to define alarms. The threshold chosen was a trade-off between detection timeliness and the number of false alarms per week. The first four BTV notifications occurred on the 12th (two notifications), 13th and 27th of July 2007. The 12th of July was considered to be the date of emergence. Alarms occurring before the 1st of March 2007 were considered to be false alarms. Using an LLR of 50, there were an average of 1.7 false alarms per week and the BTV emergence was detected seven weeks after emergence. Using an LLR of 100, there were an average of 0.8 false alarms per week and the BTV emergence was detected 9 weeks after emergence. Detection may have been delayed because of a discontinuation of milk recording between mid-July and mid-August. The first cluster with an LLR>100 located in the emergence area was further investigated. A difference between observed and predicted production of >1 kg/cow/day was observed around the time of emergence. However, a difference of equal magnitude was observed during the year preceding the outbreak. Milk production predicted from herd history alone did not allow the detection of the 2007 BTV emergence in France. Further research should be conducted on improving the prediction of test-day milk yield and on combining it with other indicators based on routinely collected data.

Evaluation of a continuous indicator for syndromic surveillance through simulation. application to vector borne disease emergence detection in cattle using milk yield.

Two vector borne diseases, caused by the Bluetongue and Schmallenberg viruses respectively, have emerged in the European ruminant populations since 2006. Several diseases are transmitted by the same vectors and could emerge in the future. Syndromic surveillance, which consists in the routine monitoring of indicators for the detection of adverse health events, may allow an early detection. Milk yield is routinely measured in a large proportion of dairy herds and could be incorporated as an indicator in a surveillance system. However, few studies have evaluated continuous indicators for syndromic surveillance. The aim of this study was to develop a framework for the quantification of both disease characteristics and model predictive abilities that are important for a continuous indicator to be sensitive, timely and specific for the detection of a vector-borne disease emergence. Emergences with a range of spread characteristics and effects on milk production were simulated. Milk yields collected monthly in 48 713 French dairy herds were used to simulate 576 disease emergence scenarios. First, the effect of disease characteristics on the sensitivity and timeliness of detection were assessed: Spatio-temporal clusters of low milk production were detected with a scan statistic using the difference between observed and simulated milk yields as input. In a second step, the system specificity was evaluated by running the scan statistic on the difference between observed and predicted milk yields, in the absence of simulated emergence. The timeliness of detection depended mostly on how easily the disease spread between and within herds. The time and location of the emergence or adding random noise to the simulated effects had a limited impact on the timeliness of detection. The main limitation of the system was the low specificity i.e. the high number of clusters detected from the difference between observed and predicted productions, in the absence of disease.