Animal Health and Welfare Branch

Guelph, Canada

Animal Health and Welfare Branch

Guelph, Canada
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Ojkic D.,University of Guelph | Hazlett M.,University of Guelph | Fairles J.,University of Guelph | Marom A.,University of Guelph | And 6 more authors.
Canadian Veterinary Journal | Year: 2015

In January, 2014, increased mortality was reported in piglets with acute diarrhea on an Ontario farm. Villus atrophy in affected piglets was confined to the small intestine. Samples of colon content were PCR-positive for porcine epidemic diarrhea virus (PEDV). Other laboratory tests did not detect significant pathogens, confirming this was the first case of PED in Canada. © 2015, Canadian Veterinary Medical Association. All rights reserved.


Dube C.,Canadian Food Inspection Agency | Ribble C.,University of Calgary | Kelton D.,University of Guelph | McNab B.,Animal Health and Welfare Branch
OIE Revue Scientifique et Technique | Year: 2011

Social networks analysis (SNA) has recently been used in veterinary epidemiology to study livestock movements. A network is obtained by considering livestock holdings as nodes in a network and movements among holdings as links among nodes. Social networks analysis enables the study of the network as a whole, exploring all the relationships among pairs of farms. Highly connected livestock holdings in the network can be identified, which can help surveillance and disease prevention activities. Observed livestock movement networks in various countries have shown an important level of contact heterogeneity and clustering (topological, not necessarily geographical or spatial) and understanding the architecture of these networks has provided a better understanding of how infections may spread. The findings of SNA studies of livestock movement should be used to build and parameterise epidemiological models of infection spread in order to improve the reliability of the outputs from these models.


McNab B.,Animal Health and Welfare Branch | Dube C.,Canadian Food Inspection Agency | Alves D.,Animal Health and Welfare Branch
OIE Revue Scientifique et Technique | Year: 2011

Frontline farm workers and veterinary-policy-makers are arguably in the best positions to influence prevention, detection, and preparedness-for-control of farm animal diseases. It is important that such individuals make biologically sound decisions concerning the daily management and regulation of the health of animals. Such decisions should be based on a good understanding of key principles of disease spread and control. This paper summarises these principles, as described in previous publications, into simple models. These models may be used to communicate concepts to readers who may not have time to study more complex models. These models illustrate the relationship between the development of new disease cases (from existing cases, i.e. the reproductive ratio R) and (i) the duration of the period during which existing cases are available as infectious, (ii) contact rates, (iii) transmission rates and (iv) susceptibility. Understanding these concepts through models has great utility, facilitating better decisions for disease prevention, detection and preparedness-for-control, before an outbreak becomes unmanageable. These basic concepts apply to all animal species, including humans.


Dorea F.C.,University of Prince Edward Island | Dorea F.C.,National Veterinary Institute SVA | McEwen B.J.,University of Guelph | McNab W.B.,Animal Health and Welfare Branch | And 2 more authors.
PLoS ONE | Year: 2013

Background: Syndromic surveillance research has focused on two main themes: the search for data sources that can provide early disease detection; and the development of efficient algorithms that can detect potential outbreak signals. Methods: This work combines three algorithms that have demonstrated solid performance in detecting simulated outbreak signals of varying shapes in time series of laboratory submissions counts. These are: the Shewhart control charts designed to detect sudden spikes in counts; the EWMA control charts developed to detect slow increasing outbreaks; and the Holt-Winters exponential smoothing, which can explicitly account for temporal effects in the data stream monitored. A scoring system to detect and report alarms using these algorithms in a complementary way is proposed. Results: The use of multiple algorithms in parallel resulted in increased system sensitivity. Specificity was decreased in simulated data, but the number of false alarms per year when the approach was applied to real data was considered manageable (between 1 and 3 per year for each of ten syndromic groups monitored). The automated implementation of this approach, including a method for on-line filtering of potential outbreak signals is described. Conclusion: The developed system provides high sensitivity for detection of potential outbreak signals while also providing robustness and flexibility in establishing what signals constitute an alarm. This flexibility allows an analyst to customize the system for different syndromes. © 2013 Dórea et al.


Dorjee S.,University of Prince Edward Island | Revie C.W.,University of Prince Edward Island | Poljak Z.,University of Guelph | McNab W.B.,Animal Health and Welfare Branch | Sanchez J.,University of Prince Edward Island
Preventive Veterinary Medicine | Year: 2013

Understanding contact networks are important for modelling and managing the spread and control of communicable diseases in populations. This study characterizes the swine shipment network of a multi-site production system in southwestern Ontario, Canada. Data were extracted from a company's database listing swine shipments among 251 swine farms, including 20 sow, 69 nursery and 162 finishing farms, for the 2-year period of 2006 to 2007. Several network metrics were generated. The number of shipments per week between pairs of farms ranged from 1 to 6. The medians (and ranges) of out-degree were: sow 6 (1-21), nursery 8 (0-25), and finishing 0 (0-4), over the entire 2-year study period. Corresponding estimates for in-degree of nursery and finishing farms were 3 (0-9) and 3 (0-12) respectively. Outgoing and incoming infection chains (OIC and IIC), were also measured. The medians (ranges) of the monthly OIC and IIC were 0 (0-8) and 0 (0-6), respectively, with very similar measures observed for 2-week intervals. Nursery farms exhibited high measures of centrality. This indicates that they pose greater risks of disease spread in the network. Therefore, they should be given a high priority for disease prevention and control measures affecting all age groups alike. The network demonstrated scale-free and small-world topologies as observed in other livestock shipment studies. This heterogeneity in contacts among farm types and network topologies should be incorporated in simulation models to improve their validity. In conclusion, this study provided useful epidemiological information and parameters for the control and modelling of disease spread among swine farms, for the first time from Ontario, Canada. © 2013 Elsevier B.V.


Amezcua M.R.,University of Guelph | Pearl D.L.,University of Guelph | Friendship R.M.,University of Guelph | Bruce McNab W.,Animal Health and Welfare Branch
Canadian Journal of Veterinary Research | Year: 2010

Branch, Ontario Ministry of Agriculture Food & Rural Affairs, Guelph, OntarioPracticing veterinarians play an important role in detecting the initial outbreak of disease in animal populations. A pilot study was conducted to determine the feasibility of a veterinary-based surveillance system for the Ontario swine industry. A total of 7 practitioners from 5 clinics agreed to submit information from July 1, 2007 to June 30, 2008. The surveillance program was evaluated in terms of timeliness, compliance, geographic coverage, and data quality. Our study showed that the veterinary-based surveillance system was acceptable to practitioners and produced useful data. The program obtained information from 25% of pig farms in Ontario during this time period. However, better communication with practitioners, more user-friendly recording systems that can be adapted to each clinic's management system, active involvement of the clinics' technical personnel, and the use of financial incentives may help to improve compliance and timeliness.


Dorea F.C.,University of Prince Edward Island | Muckle C.A.,University of Prince Edward Island | Kelton D.,University of Guelph | McClure J.T.,University of Prince Edward Island | And 4 more authors.
PLoS ONE | Year: 2013

Background: Recent focus on earlier detection of pathogen introduction in human and animal populations has led to the development of surveillance systems based on automated monitoring of health data. Real- or near real-time monitoring of pre-diagnostic data requires automated classification of records into syndromes-syndromic surveillance-using algorithms that incorporate medical knowledge in a reliable and efficient way, while remaining comprehensible to end users. Methods: This paper describes the application of two of machine learning (Naïve Bayes and Decision Trees) and rule-based methods to extract syndromic information from laboratory test requests submitted to a veterinary diagnostic laboratory. Results: High performance (F1-macro = 0.9995) was achieved through the use of a rule-based syndrome classifier, based on rule induction followed by manual modification during the construction phase, which also resulted in clear interpretability of the resulting classification process. An unmodified rule induction algorithm achieved an F1-micro score of 0.979 though this fell to 0.677 when performance for individual classes was averaged in an unweighted manner (F1-macro), due to the fact that the algorithm failed to learn 3 of the 16 classes from the training set. Decision Trees showed equal interpretability to the rule-based approaches, but achieved an F1-micro score of 0.923 (falling to 0.311 when classes are given equal weight). A Naïve Bayes classifier learned all classes and achieved high performance (F1-micro = 0.994 and F1-macro =. 955), however the classification process is not transparent to the domain experts. Conclusion: The use of a manually customised rule set allowed for the development of a system for classification of laboratory tests into syndromic groups with very high performance, and high interpretability by the domain experts. Further research is required to develop internal validation rules in order to establish automated methods to update model rules without user input. © 2013 Dorea et al.


PubMed | University of Prince Edward Island, University of Guelph and Animal Health and Welfare Branch
Type: Journal Article | Journal: PloS one | Year: 2013

Syndromic surveillance research has focused on two main themes: the search for data sources that can provide early disease detection; and the development of efficient algorithms that can detect potential outbreak signals.This work combines three algorithms that have demonstrated solid performance in detecting simulated outbreak signals of varying shapes in time series of laboratory submissions counts. These are: the Shewhart control charts designed to detect sudden spikes in counts; the EWMA control charts developed to detect slow increasing outbreaks; and the Holt-Winters exponential smoothing, which can explicitly account for temporal effects in the data stream monitored. A scoring system to detect and report alarms using these algorithms in a complementary way is proposed.The use of multiple algorithms in parallel resulted in increased system sensitivity. Specificity was decreased in simulated data, but the number of false alarms per year when the approach was applied to real data was considered manageable (between 1 and 3 per year for each of ten syndromic groups monitored). The automated implementation of this approach, including a method for on-line filtering of potential outbreak signals is described.The developed system provides high sensitivity for detection of potential outbreak signals while also providing robustness and flexibility in establishing what signals constitute an alarm. This flexibility allows an analyst to customize the system for different syndromes.

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