Financial resources may limit the number of samples that can be collected and analysed in disease surveillance programmes. When the aim of surveillance is disease detection and identification of case herds, a risk-based approach can increase the sensitivity of the surveillance system. In this paper, the association between two network analysis measures, i.e. 'in-degree' and 'ingoing infection chain', and signs of infection is investigated. It is shown that based on regression analysis of combined data from a recent cross-sectional study for endemic viral infections and network analysis of animal movements, a positive serological result for bovine coronavirus (BCV) and bovine respiratory syncytial virus (BRSV) is significantly associated with the purchase of animals. For BCV, this association was significant also when accounting for herd size and regional cattle density, but not for BRSV. Examples are given for different approaches to include cattle movement data in risk-based surveillance by selecting herds based on network analysis measures. Results show that compared to completely random sampling these approaches increase the number of detected positives, both for BCV and BRSV in our study population. It is concluded that network measures for the relevant time period based on updated databases of animal movements can provide a simple and straight forward tool for risk-based sampling.
Intro: Belgium gained the bovine tuberculosis (bTB) officially free (OTF) status in 2003 [1]. The present study was carried out in order to evaluate the different surveillance components of the current bTB surveillance program and, to estimate how this program could be optimized in accordance with European legislation [2].M&M: Separate scenario trees were designed for each component of the surveillance program. Surveillance data over the past 5 years were collected, as well as population and movement's data. Different stochastic simulations were carried out to measure the impact of modifications in each surveillance component, regarding the diagnostic test used and the fraction of population sampled, towards the animal, herd and component level sensitivities (ModelRisk).Results-Discussion: The sensitivity (mode) for the following 3 surveillance components was respectively 0.92 for testing 50% at slaughterhouse, 0.87 for testing 50% of purchased animals, and 0.20 for testing all animals during the winter screening. Large variations around the average values were observed. The sensitivity analysis showed that the most influential parameter explaining this variability came from the uncertainty distribution around the diagnostic process parameter.
Potential chemical food safety incidents have been investigated by Veterinary Laboratories Agency (VLA) on behalf of Food Standards Agency (FSA) since 1990. This paper describes the criteria on which these incidents are selected and the type and numbers of incidents investigated over the last two decades.
The management of public health emergencies is improved by quick, exhaustive and standardized flow of data on disease outbreaks, by using specific tools for data collection, registration and analysis. In this context, the National Information System for the Notification of Outbreaks of Animal Diseases (SIMAN) has been developed in Italy to collect and share data on the notifications of outbreaks of animal diseases. SIMAN is connected through web services to the national database of animals and holdings (BDN) and has been integrated with tools for the management of epidemic emergencies. The website has been updated with a section dedicated to the contingency planning in case of epidemic emergency. EpiTrace is one such useful tool also integrated in the BDN and based on the Social Network Analysis (SNA) and on network epidemiological models. This tool gives the possibility of assessing the risk associated to holdings and animals on the basis of their trade, in order to support the veterinary services in tracing back and forward the animals in case of outbreaks of infectious diseases.
The OMAR project has been launched in 2009 to model livestock mortality in space and time, and design a monitoring system able to detect anomalies possibly associated with health events. Since our first results seemed to confirm the interest of mortality as unspecific surveillance indicator, we are currently implementing a near real-time cattle mortality monitoring system, based on data collected daily by rendering plants.
Documenting freedom from disease is important for early detection and for maintenance of export. In Denmark, 34,974 blood samples were tested for Aujeszky's disease in 2008. By the use of a scenario tree model, it is demonstrated that after one year testing, the probability of Denmark being free from Aujeszky's disease is reduced from >0.99 to >0.98 or >0.96, when the number of samples is reduced by 28% or 43%, respectively.
We analysed in this paper the relevance of an approach consisting in several retrospective cross-sectional surveys gathering data about the last two antibiotic treatments to monitor antimicrobial use in ruminants. This type of surveys allows to qualitatively study real antibiotics use in field conditions in each animal sectors. It provides information about antimicrobial compounds used given the disease and type of production of the animal, and quantifies extra-label use. Hence such an approach is useful, but should be used as a complementary tool of quantitative analyses measuring the amount of antimicrobial drugs use per animal sector. A legislative framework would be needed to better collect data about antimicrobial use in France.
An ecologic surveillance system for West Nile was implemented in Catalonia in 2007. This system consisted of different components: active and passive avian surveillance, follow-up of chicken sentinels, cross-sectional surveys in feral equines, follow-up of equine sentinels, passive equine surveillance, and entomological surveillance. Until 2010 these activities have been continuously adapted to improve the efficiency and the sensitivity of the surveillance. Between 2007 and 2010 the active WNV infection was not detected in any component.
Veterinary surveillance strategy depends on a variety of epidemiological information, which enhances the complexity of such a decision-making task. A possible solution is the development of a decision support system aimed at retrieving data from different databases and information sources and analyzing them in order to provide useful and explicit information. On the basis of this concept, a data warehouse combined with a geo-data mart system is presented in this paper as a tool to support the surveillance strategy of the veterinary services of the Veneto Region in Italy.
In Italy in the frame of active surveillance of Scrapie six different rapid tests have been used leading to the identification of both classical and atypical Scrapie cases. Despite all the 69 Italian atypical cases (found mostly in sheep) were identified by one rapid test (Biorad TESeE), the assumption that the different diagnostic kits used in surveillance of TSEs were equally able to identify the atypical form was investigated. In particular, we wanted to check whether the number of animals tested by some of the tests could have been so small as to be consistent with the lack of identified cases of the disease, assuming equal diagnostic sensitivity. Using regression models to compare each test with Biorad TESeE, only the small number of IDEXX tests carried out is consistent with the identification of zero AS cases.
A two stage processing method using Poisson regression and an Autoregressive Integrated Moving Average (ARIMA) model was developed to improve the performance of the temporal outbreak detection methods.
The European Union (EU) Commission along with the 27 Member States is constructing a future Animal Health Law following the commitments of the EU Animal Health Strategy (2007-2013). The aim of this law is to lay down the general principles of animal health, animal health requirements for trade of live animals and their products and also to set the principles and measures for disease control. In this context, animal disease surveillance and biosecurity will be the main tools to achieve the prevention approach of the law. The Spanish Presidency of the EU during 2010 carried out a survey in order to assess the current EU disease surveillance programs. The results of the survey provided inputs from the Member States to the Commission in the process of constructing the new Animal Health Law.The finding of this study along with a technical seminar held in April 2010 in Seville acknowledged disease surveillance as key element in any animal health legal requirement. Therefore it should be a pillar of the future EU Animal Health Law.
Foot and Mouth Disease is endemic in southern Africa and is associated with wild African buffalo populations that act as carriers of the disease. A study to determine the grazing patterns of both cattle and buffaloes was conducted to better predict their occurrence and hence possible interspecies contact to affect disease transmission. Moreover, disease control measures, such as fencing and vaccination was included in a Bayesian probabilistic framework to estimate the spatio-temporal risk of contact and possible transmission between these two species. The model employs multi-dimensional Kriging and random walk predictions in conjunction with static and dynamic covariates, including remotely sensed data. The model has given realistic outputs and will be further developed to inform a space-time information system to aid decision makers in prioritizing surveillance and control measures.
Decision support models for five endemic diseases were developed to simulate control and/or eradication options for-these diseases. Based on bi-yearly prevalence surveys and new developments, the models are adapted. The model results facilitated risk communication to stakeholders. The process. is illustrated with the results for BVDV.
