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Introduction: Predicting which species are susceptible to viruses (i.e., host range) is important for understanding and developing effective strategies to control viral outbreaks in both humans and animals. The use of machine learning and bioinformatic approaches to predict viral hosts has been expanded with advancements in in-silico techniques. We conducted a scoping review to identify the breadth of machine learning methods applied to influenza and coronavirus genome data for the identification of susceptible host species. Methods: The protocol for this scoping review is available at https://hdl.handle.net/10214/26112. Five online databases were searched, and 1,217 citations, published between January 2000 and May 2022, were obtained, and screened in duplicate for English language and in-silico research, covering the use of machine learning to identify susceptible species to viruses. Results: Fifty-three relevant publications were identified for data charting. The breadth of research was extensive including 32 different machine learning algorithms used in combination with 29 different feature selection methods and 43 different genome data input formats. There were 20 different methods used by authors to assess accuracy. Authors mostly used influenza viruses (n = 31/53 publications, 58.5%), however, more recent publications focused on coronaviruses and other viruses in combination with influenza viruses (n = 22/53, 41.5%). The susceptible animal groups authors most used were humans (n = 57/77 analyses, 74.0%), avian (n = 35/77 45.4%), and swine (n = 28/77, 36.4%). In total, 53 different hosts were used and, in most publications, data from multiple hosts was used. Discussion: The main gaps in research were a lack of standardized reporting of methodology and the use of broad host categories for classification. Overall, approaches to viral host identification using machine learning were diverse and extensive. [ABSTRACT FROM AUTHOR]

Background: Carbapenem‐resistant Enterobacterales (CRE) are a concern in both human and animal medicine globally. Despite extensive research in humans, limited data exist on CRE in companion animals, with a lack of nationwide prevalence estimates. Hypothesis/Objectives: To assess the occurrence and trends of CRE in cats and dogs across the United States by analyzing 4 years of commercial antimicrobial susceptibility testing (AST) data. Animals: Between 2019 and 2022, 477 426 ASTs were conducted on Enterobacterales isolates against imipenem. Isolates were derived from 379 598 dogs and 97 828 cats. Animal origin was not disclosed. Methods: In this retrospective study, antimicrobial susceptibility test data from IDEXX Laboratories were analyzed. Analysis included resistance estimations to imipenem stratified by sampling site, an assessment of resistance patterns over time and location, and the application of space‐time cluster analysis to identify potential outbreaks. Antibiograms were produced for carbapenem‐resistant isolates. Results: Susceptibility to imipenem was high, at 98.86%. Temporal analysis indicated stability in susceptibility, with an unexplained reduction in susceptible isolates in June 2019. Spatial analysis identified 2 high‐risk clusters along the Western Coast (relative risk [RR]: 23.26; P <.001) and in Texas (RR: 10.72; P <.001) in that month. Three other clusters were found, in Missouri (RR: 39.55; P =.038), Florida (RR: 4.53; P <.001), and New York (RR: 9.20; P <.001). Conclusions and Clinical Importance: CRE are present at a low prevalence in dogs and cats across the United States. Variations in prevalence across patient‐level and environmental factors highlight the need for tailored stewardship programs. [ABSTRACT FROM AUTHOR]

Background: Throughout the SARS-CoV-2 pandemic, policymakers have had to navigate between recommending voluntary behaviour change and policy-driven behaviour change to mitigate the impact of the virus. While individuals will voluntarily engage in selfprotective behaviour when there is an increasing infectious disease risk, the extent to which this occurs and its impact on an epidemic is not known. Methods: This paper describes a deterministic disease transmission model exploring the impact of individual avoidance behaviour and policy-mediated avoidance behaviour on epidemic outcomes during the second wave of SARS-CoV-2 infections in Ontario, Canada (September 1, 2020 to February 28, 2021). The model incorporates an information feedback function based on empirically derived behaviour data describing the degree to which avoidance behaviour changed in response to the number of new daily cases COVID-19. Results: Voluntary avoidance behaviour alone was estimated to reduce the final attack rate by 23.1%, the total number of hospitalizations by 26.2%, and cumulative deaths by 27.5% over 6 months compared to a counterfactual scenario in which there were no interventions or avoidance behaviour. A provincial shutdown order issued on December 26, 2020 was estimated to reduce the final attack rate by 66.7%, the total number of hospitalizations by 66.8%, and the total number of deaths by 67.2% compared to the counterfactual scenario. Conclusion: Given the dynamics of SARS-CoV-2 in a pre-vaccine era, individual avoidance behaviour in the absence of government action would have resulted in a moderate reduction in disease however, it would not have been sufficient to entirely mitigate transmission and the associated risk to the population in Ontario. Government action during the second wave of the COVID-19 pandemic in Ontario reduced infections, protected hospital capacity, and saved lives. [ABSTRACT FROM AUTHOR]

Introduction: Public health preparedness is based on timely and accurate information. Time series forecasting using disease surveillance data is an important aspect of preparedness. This study compared two approaches of time series forecasting: seasonal auto‐regressive integrated moving average (SARIMA) modelling and the artificial neural network (ANN) algorithm. The goal was to model weekly seasonal influenza activity in Canada using SARIMA and compares its predictive accuracy, based on root mean square prediction error (RMSE) and mean absolute prediction error (MAE), to that of an ANN. Methods: An initial SARIMA model was fit using automated model selection by minimizing the Akaike information criterion (AIC). Further inspection of the autocorrelation function and partial autocorrelation function led to 'manual' model improvements. ANNs were trained iteratively, using an automated process to minimize the RMSE and MAE. Results: A total of 378, 462 cases of influenza was reported in Canada from the 2010–2011 influenza season to the end of the 2019–2020 influenza season, with an average yearly incidence risk of 20.02 per 100,000 population. Automated SARIMA modelling was the better method in terms of forecasting accuracy (per RMSE and MAE). However, the ANN correctly predicted the peak week of disease incidence while the other models did not. Conclusion: Both the ANN and SARIMA models have shown to be capable tools in forecasting seasonal influenza activity in Canada. It was shown that applying both in tandem is beneficial, SARIMA better forecasted overall incidence while ANN correctly predicted the peak week. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Veterinary Research / Revue Canadienne de Recherche Vétérinaire is the property of Canadian Veterinary Medical Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The advancement of web-based technologies makes it possible to build user interfaces or web pages that present and summarize complex data in easy-to-read graphical formats that emphasize key information. Taking advantage of this technologic progress, we addressed the need for real-time visualizations of trends for major pathogens in the largest livestock industries in Ontario: poultry, swine, and cattle. These visualizations were built using test data from the laboratory information management system of the Animal Health Laboratory at the University of Guelph, a large veterinary diagnostic laboratory in Ontario. The data were processed using R software and used to construct interactive and dynamic visualizations using Tableau Desktop v.2021.4 (Tableau Software). We designed 12 dashboards: in chickens—influenza A virus, fowl adenovirus, infectious bronchitis virus, and infectious laryngotracheitis virus; in turkeys—influenza A virus; in swine, influenza A virus, rotavirus, and porcine reproductive and respiratory syndrome virus; in cattle—bovine viral diarrhea virus, Mycoplasma bovis, Salmonella Dublin in individual samples, and Salmonella Dublin in bulk tank milk samples. Data for each pathogen are presented in 2 dashboards. One shows the data of the last 10 y (general view) and the other the data of the last 3 y, but in more detail (comprehensive view). Information on gaining access to all dashboards is available at https://iapd.lsd.uoguelph.ca/. The visualizations provide near-real-time access to aggregated assay results for selected pathogens for veterinarians, animal health regulatory agencies, researchers, and other users who are interested in livestock pathogen surveillance. [ABSTRACT FROM AUTHOR]

Simple Summary: Vaccinated mother pigs can pass vaccine protection (immunity) onto their offspring, but in the case of influenza, the benefit is not clear. Influenza viruses evolve rapidly and can sometimes change their appearance to the point of becoming a new stain that evades a pig's immune defenses. It is believed that matching the vaccine to the virus is important for protection. To predict if a vaccine is protective it is, therefore, also important to know how closely it matches the influenza strain or strains circulating in the herd. For the first time, we apply the methods of evidence-based medicine to understand if vaccinating sows results in the protection of their piglets from influenza. We were only interested in studies where the researcher knew if the vaccine matched the infecting virus. These types of studies are called challenge trials because researchers controlled both the vaccines used in the mother pigs and the strain of influenza viruses used to infect the piglets. We looked at scientific studies published over twenty years and considered many ways to measure protection. We found piglets from vaccinated sows took a little bit longer to shed the virus if they became infected and that less virus was found in piglets where their mother's vaccine matched the virus used to infect the piglet. In modern commercial farms, however, piglets are often exposed back-to-back or at the same time to the same, or more often, to more than one strain of influenza. Also, viruses behave differently in herds than they do in small studies because the number of pigs in a herd is many times greater. Because most studies involved simple exposures in small groups of pigs and only one small study looked at a back-to-back exposure with the same strain of influenza, it is difficult to know if our findings can be further extended into the real world. Despite this, the body of research was useful to show the importance of matching. We also learned that additional research is still needed and importantly, that there is room for improvement in how influenza vaccine studies in pigs are reported. Influenza vaccine research is complex in pigs and it is important to understand the type or types of virus strains involved in each study. Future research is needed where researchers are able to identify all infecting strains of influenza and the piglets experience real-world influenza virus exposures. It is unclear if piglets benefit from vaccination of sows against influenza. For the first time, methods of evidence-based medicine were applied to answer the question: "Does vaccine-induced maternally-derived immunity (MDI) protect swine offspring against influenza A viruses?". Challenge trials were reviewed that were published from 1990 to April 2021 and measured at least one of six outcomes in MDI-positive versus MDI-negative offspring (hemagglutination inhibition (HI) titers, virus titers, time to begin and time to stop shedding, risk of infection, average daily gain (ADG), and coughing) (n = 15). Screening and extraction of study characteristics was conducted in duplicate by two reviewers, with data extraction and assessment for risk of bias performed by one. Homology was defined by the antigenic match of vaccine and challenge virus hemagglutinin epitopes. Results: Homologous, but not heterologous MDI, reduced virus titers in piglets. There was no difference, calculated as relative risks (RR), in infection incidence risk over the entire study period; however, infection hazard (instantaneous risk) was decreased in pigs with MDI (log HR = −0.64, 95% CI: −1.13, −0.15). Overall, pigs with MDI took about a ½ day longer to begin shedding virus post-challenge (MD = 0.51, 95% CI: 0.03, 0.99) but the hazard of infected pigs ceasing to shed was not different (log HR = 0.32, 95% CI: −0.29, 0.93). HI titers were synthesized qualitatively and although data on ADG and coughing was extracted, details were insufficient for conducting meta-analyses. Conclusion: Homology of vaccine strains with challenge viruses is an important consideration when assessing vaccine effectiveness. Herd viral dynamics are complex and may include concurrent or sequential exposures in the field. The practical significance of reduced weaned pig virus titers is, therefore, not known and evidence from challenge trials is insufficient to make inferences on the effects of MDI on incidence risk, time to begin or to cease shedding virus, coughing, and ADG. The applicability of evidence from single-strain challenge trials to field practices is limited. Despite the synthesis of six outcomes, challenge trial evidence does not support or refute vaccination of sows against influenza to protect piglets. Additional research is needed; controlled trials with multi-strain concurrent or sequential heterologous challenges have not been conducted, and sequential homologous exposure trials were rare. Consensus is also warranted on (1) the selection of core outcomes, (2) the sizing of trial populations to be reflective of field populations, (3) the reporting of antigenic characterization of vaccines, challenge viruses, and sow exposure history, and (4) on the collection of non-aggregated individual pig data. [ABSTRACT FROM AUTHOR]

There is a need for wildlife professionals to work together to more effectively protect wildlife health; however, collaboration between people of different professions and institutions can be challenging. Our objective was to examine and compare how the conservation and animal health fields share wildlife health data and information in Ontario, Canada. Fifty-five (50%) of 110 invited participants completed an online questionnaire about their wildlife health communication network over the previous 12 months. The professional communication networks (i.e., ego-networks) of practitioners were compared with respect to data sharing between institutions and professions. Results showed low levels of tie dispersion and high levels of ego-alter similarity. There was more data and information sharing occurring between people of different institutions than between people of different professions based on comparing tie dispersion and ego-alter similarity values for these categories. This study highlights opportunities for improved networking between wildlife conservation and health practitioners in Ontario. [ABSTRACT FROM AUTHOR]

Introduction: Group A rotaviruses are major pathogens in causing severe diarrhea in young children and neonates of many different species of animals worldwide and group A rotavirus sequence data are becoming increasingly available over time. Different methods exist that allow for rotavirus genotyping, but machine learning methods have yet to be explored. Usage of machine learning algorithms such as random forest alongside alignment-based methodology may allow for both efficient and accurate classification of circulating rotavirus genotypes through the dual classification system. Methods: Random forest models were trained on positional features obtained from pairwise and multiple sequence alignment and cross-validated using methods of repeated 10-fold cross-validation thrice and leave one- out cross validation. Models were then validated on unseen data from the testing datasets to observe real-world performance. Results: All models were found to perform strongly in classification of VP7 and VP4 genotypes with high overall accuracy and kappa values during model training (0.975–0.992, 0.970–0.989) and during model testing (0.972–0.996, 0.969–0.996), respectively. Models trained on multiple sequence alignment generally had slightly higher overall accuracy and kappa values than models trained on pairwise sequence alignment method. In contrast, pairwise sequence alignment models were found to be generally faster than multiple sequence alignment models in computational speed when models do not need to be retrained. Models that used repeated 10-fold cross-validation thrice were also found to be much faster in model computational speed than models that used leave-one-out cross validation, with no noticeable difference in overall accuracy and kappa values between the cross-validation methods. Discussion: Overall, random forest models showed strong performance in the classification of both group A rotavirus VP7 and VP4 genotypes. Application of these models as classifiers will allow for rapid and accurate classification of the increasing amounts of rotavirus sequence data that are becoming available. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Veterinary Journal / Revue Vétérinaire Canadienne is the property of Canadian Veterinary Medical Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Journal of Veterinary Research / Revue Canadienne de Recherche Vétérinaire is the property of Canadian Veterinary Medical Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Data on antimicrobial use (AMU) in pig production are needed for the development of good antimicrobial stewardship practices to reduce the risk of antimicrobial resistance in bacteria that can cause illness in animals and humans. In Canada, there is a lack of quantitative data on AMU in the farrowing and nursery stages of pig production. This study aimed to determine which antimicrobial active ingredients are currently used in farrowing, nursery, and grower-finisher herds in the province of Ontario, Canada, and to quantify AMU using various metrics. We collected data on herd demographics, biosecurity, health status, and AMU during one production cycle from 25 farrowing and 25 nursery herds in Ontario, between May 2017 and April 2018, and obtained data from 23 Ontario grower-finisher herds during the same time frame from the Public Health Agency's Canadian Integrated Program for Antimicrobial Resistance Surveillance. We applied frequency measures, and weight-, and dose-based metrics to the data. Results: In all pigs, the highest quantity of AMU was administered in-feed. By all routes of administration and compared to other production stages, nursery pigs used more antimicrobials in mg/kg biomass and the number of Canadian defined daily doses per 1000 pig-days (doseCA rate), while grower-finisher pigs used more antimicrobials in total kilograms and the number of Canadian defined daily doses per pig. In suckling pigs in some herds, there was routine disease prevention use of ceftiofur, an antimicrobial active ingredient categorized as very highly important in human medicine by Health Canada. The top antimicrobial used in each stage of pig production often varied by the metric used. There was producer-reported growth promotion use of antimicrobials in suckling and grower-finisher feed. Conclusions: The results of this study provide a current picture of AMU in pigs in Ontario and can be used as a basis for further research on AMU in farrowing and nursery herds in Canada. Our findings confirm that it would be useful to include farrowing and nursery herds in routine AMU surveillance in Canada. A future analysis using data from this project will examine factors that affect the quantity of AMU. [ABSTRACT FROM AUTHOR]

Observational studies describing the impact of transport duration on weaned piglet welfare are limited. Current Canadian transport regulations are heavily informed by studies involving market hogs. Due to physiological differences between weaned piglets and market hogs, additional data on their response to transport are needed for age-specific evidence-based recommendations. A cohort study was conducted to describe and compare mortality, injury, weight change, hematological or biochemical changes in hydration, muscle injury and stress response observed in weaned piglets undergoing short duration (SD, <3 h), or long duration (LD, >30 h) commercial summertime transport events. Data collection on 440 of 11,434 transported piglets occurred the morning of the day before transport (T0), at arrival (T1) and approximately 3 to 4 d (78 to 93 h) after arrival at the nursery barn (T2). Low mortality occurred over all transport events (0.06%) with no association observed between transport duration and odds of death during transport (P = 0.62). The incidence of lameness between T0 and T1 was low (1.84% of the 435 focal piglets scored) with all lameness cases identified as mild in severity. Lesions on ears and skin were more prevalent than other injury types after transport (T1) and may have been related to mixing aggression associated with weaning rather than transport alone. LD piglets weighed 0.39 kg less than SD piglets at T1 (P < 0.01), but no difference in group weight was observed at T2 (P = 0.17). Hematological and biochemical differences were present between groups at T1. LD piglets had increased hematocrit levels compared with SD piglets (P = 0.01), suggesting increased body water losses. SD piglets showed greater levels of muscle injury compared with LD piglets including elevated aspartate aminotransferase (P < 0.01) and creatine kinase (P < 0.01). However, these parameters were within normal reference ranges for piglets of this age group. Indicators of physiological stress response including cortisol and neutrophil to lymphocyte ratios were elevated in SD piglets compared with LD piglets (P = 0.02 and P < 0.01, respectively). The results of this study demonstrate that both short and long transport durations can result in detectable physiological changes in weaned piglets. The overall impact of these durations on piglet welfare should be further explored by analyzing behavioral time budgets during and after transport. [ABSTRACT FROM AUTHOR]

Background: A variety of public health measures have been implemented during the COVID-19 pandemic in Canada to reduce contact between individuals. The objective of this study was to provide empirical contact pattern data to evaluate the impact of public health measures, the degree to which social contacts rebounded to normal levels, as well as direct public health efforts toward age- and location-specific settings.Methods: Four population-based cross-sectional surveys were administered to members of a paid panel representative of Canadian adults by age, gender, official language, and region of residence during May (Survey 1), July (Survey 2), September (Survey 3), and December (Survey 4) 2020. A total of 4981 (Survey 1), 2493 (Survey 2), 2495 (Survey 3), and 2491 (Survey 4) respondents provided information about the age and setting for each direct contact made in a 24-h period. Contact matrices were constructed and contacts for those under the age of 18 years imputed. The next generation matrix approach was used to estimate the reproduction number (Rt) for each survey. Respondents with children under 18 years estimated the number of contacts their children made in school and extracurricular settings.Results: Estimated Rt values were 0.49 (95% CI: 0.29-0.69) for May, 0.48 (95% CI: 0.29-0.68) for July, 1.06 (95% CI: 0.63-1.52) for September, and 0.81 (0.47-1.17) for December. The highest proportion of reported contacts occurred within the home (51.3% in May), in 'other' locations (49.2% in July) and at work (66.3 and 65.4% in September and December). Respondents with children reported an average of 22.7 (95% CI: 21.1-24.3) (September) and 19.0 (95% CI 17.7-20.4) (December) contacts at school per day per child in attendance.Conclusion: The skewed distribution of reported contacts toward workplace settings in September and December combined with the number of reported school-related contacts suggest that these settings represent important opportunities for transmission emphasizing the need to support and ensure infection control procedures in both workplaces and schools. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Veterinary Journal / Revue Vétérinaire Canadienne is the property of Canadian Veterinary Medical Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Influenza A viruses (IAVs) are common causes of respiratory infection in pigs. The objective of this study was to characterize the circulation of IAVs between weaning and market age on the basis of development of antibody response and molecular epidemiology of detected viruses. Two batches of weaned pigs were followed in the nursery and finisher barns with a sample of 81 and 75 pigs. Nasal swabs and blood samples were collected from individual pigs for virological and serological analyses. A H3N2 subtype virus, of cluster IV, was detected in Study 1, with a maximum of 97.9% identity to HA gene of viruses previously isolated in Ontario. In Study 2, a H1N1 subtype virus, of 2009 H1N1 pandemic lineage, was detected, with a maximum of 97.8% identity to HA gene of viruses previously isolated in Ontario. On the basis of HA gene, it was observed that pigs were being detected with the same virus over time. The existence of antibody titers for IAV other than the isolated one confirmed that more than one subtype can circulate in the same population. In Study 1, pigs with higher numbers of IAV detection had lower serological titers for the same virus that was confirmed to circulate in the nursery (P < 0.01). Thorough knowledge of all endemic viral strains is fundamental for development of infection and disease control, particularly in complex production systems. This may include consideration of sampling and testing strategies which could detect circulation of all IAV variants, even if they have low prevalence. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Veterinary Research / Revue Canadienne de Recherche Vétérinaire is the property of Canadian Veterinary Medical Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Avian influenza viruses can cause economically devastating diseases in poultry and have the potential for zoonotic transmission. To mitigate the consequences of avian influenza, disease prediction systems have become increasingly important. In this study, we have proposed a framework for the prediction of the occurrence and spread of avian influenza events in a geographical area. The application of the proposed framework was examined in an Indonesian case study. An extensive list of historical data sources containing disease predictors and target variables was used to build spatiotemporal and transactional datasets. To combine disparate sources, data rows were scaled to a temporal scale of 1-week and a spatial scale of 1-degree × 1-degree cells. Given the constructed datasets, underlying patterns in the form of rules explaining the risk of occurrence and spread of avian influenza were discovered. The created rules were combined and ordered based on their importance and then stored in a knowledge base. The results suggested that the proposed framework could act as a tool to gain a broad understanding of the drivers of avian influenza epidemics and may facilitate the prediction of future disease events. [ABSTRACT FROM AUTHOR]

Background: Food animal veterinarians face commodity specific and urgent global challenges yet conditions preventing use of best available knowledge have been sparsely studied. The American Association of Swine Veterinarians (AASV) membership (N = 1289) was surveyed online to benchmark their information priorities and their motivations and sources for keeping current with infectious disease research, and to describe their reported time, skill, access, and process as barriers to knowledge translation (KT). Results: Respondents (n = 80) were mostly from Canada (n = 40) and the U.S.A (n = 31) and demographics approximated the AASV's. Colleagues are the first choice for information on difficult cases (49%, 95%CI: 38–61). Half of respondents (53%, 95%CI: 41–64) spend an hour or less per week keeping up with infectious disease research. The majority reported moderate or less than moderate efficiency (62%, 95%CI: 51–72), and moderate or greater stress (59%, 95%CI: 48–70) with their process for keeping up. Journal article methods sections are commonly not read, almost a third (32%, 95% CI: 22–43) reported either they do not evaluate statistical methods or that they had poor confidence to do so, and half (52, 95%CI: 41–63) could not explain 'confounding bias'. Approximately half (55%, 95%CI: 41-69) with direct oversight of swine herds had full access to 2 or fewer academic journals. Approximately a third of respondents (34%, 95%CI: 24–46) selected only formats involving single research studies (either full text or summaries) as preferred reading materials for keeping current over expert summaries of the body of evidence. Conclusion: KT barriers are considerable and a source of stress for many swine veterinarians. Sub-optimal efficiency with keeping up and low confidence to appraise aspects of research are concerns. Results are consistent with previous literature and illustrate need for improved KT infrastructure and for additional training in statistical methods and interpretation of primary research. Further evaluation is warranted of why approximately a third of veterinarians in this study, for the purpose of keeping up, preferentially choose to review individual research studies over choices that would include an expert summary of the body of evidence. Consideration of reasons for this preference will be important in the planning of KT infrastructure improvements. [ABSTRACT FROM AUTHOR]

Background: Influenza A viruses of swine (IAV-S) are a global zoonotic and economic concern. Primary control is through vaccination yet a formal evidence map summarizing vaccine research conducted in pigs is not available. Objective: Ten characteristics of English language primary IAV-S vaccine research, conducted at the level of the pig or higher, were charted to identify research gaps, topics for systematic review, and coverage across different publication types. Design: Six online databases and grey literature were searched, without geographic, population, or study type restrictions, and abstracts screened independently and in duplicate for relevant research published between 1990 and May 2018. Full text data was charted by a single reviewer. Results: Over 11,000 unique citations were screened, identifying 376 for charting, including 175 proceedings from 60 conferences, and 170 journal articles from 51 journals. Reported outcomes were heterogeneous with measures of immunity (86%, n = 323) and virus detection (65%, n = 246) reported far more than production metrics (9%, n = 32). Study of transmissibility under conditions of natural exposure (n = 7), use of mathematical modelling (n = 11), and autogenous vaccine research reported in journals (n = 7), was limited. Conclusions: Most research used challenge trials (n = 219) and may have poor field relevance or suitability for systematic review if the purpose is to inform clinical decisions. Literature on vaccinated breeding herds (n = 89) and weaned pigs (n = 136) is potentially sufficient for systematic review. Research under field conditions is limited, disproportionately reported in conference proceedings versus journal articles, and may be insufficient to support systematic review. [ABSTRACT FROM AUTHOR]

Background: The heightened prevalence of Salmonella Typhimurium remains a public health and food safety concern. Studies have reported antibiotic, flavophospholipol, may have the ability to reduce Salmonella in swine, as well as alter the gut microbiota in favour of beneficial bacteria by inhibiting pathogenic bacteria. Thus, the objective of this study was to investigate the fecal microbiota of weaned pigs receiving in-feed flavophospholipol and challenged with Salmonella Typhimurium. Results: Twenty-one weaned pigs were fed either a diet containing 4 ppm of flavophospholipol (treatment group) or a non-medicated feed (control group) for 36 days post-weaning (Day 1 to Day 36). The pigs were orally challenged with a 2 mL dose of 108 CFU/mL of S. Typhimurium at Day 7 and Day 8. Community bacterial DNA extracted from fecal samples collected at Day 6 (before challenge) and Day 36 (28 days after challenge) were used to assess the fecal microbiota using the V4 region of the 16S rRNA gene with Illumina MiSeq next-generation sequencing. Sequencing data were visualized using mothur and analyzed in JMP and R software. The fecal microbiota of pigs in the treatment group had differences in abundance of phyla (Firmicutes, Proteobacteria) and genera (Lactobacillus, Roseburia, Treponema, unclassified Ruminococcaceae, Blautia, Streptococcus, Megasphaera, Dorea, Sporobacter, Peptococcus, unclassified Firmicutes, Clostridium IV and Campylobacter) when compared to pigs that were controls, 28 days after challenge with Salmonella (P < 0.05). Specifically, results demonstrated a significant increase in phylum Proteobacteria (P = 0.001) and decrease in Firmicutes (P = 0.012) and genus Roseburia (P = 0.003) in the treated pigs suggestive of possible microbial dysbiosis. An increased abundance of genera Lactobacillus (P = 0.012) was also noted in the treated group in comparison to the control. Conclusion: Based on these findings, it is difficult to conclude whether treatment with 4 ppm of flavophospholipol is promoting favorable indigenous bacteria in the pig microbiota as previous literature has suggested. [ABSTRACT FROM AUTHOR]

The objectives of this research were to describe the contact structure of transportation vehicles and swine facilities in an Ontario swine production system, and to assess their potential contribution to possible disease transmission over different time periods. A years' worth of data (2015) was obtained from a large swine production and data management company located in Ontario, Canada. There was a total of 155 different transportation vehicles, and 220 different farms within the study population. Two-mode networks were constructed for 1-,3-, and 7-day time periods over the entire year and were analyzed. Trends in the size of the maximum weak component and outgoing contact chain over discrete time periods were investigated using linear regression. Additionally, the number of different types of facilities with betweenness >0 and in/out degree>0 were analyzed using Poisson regression. Maximum weekly outgoing contact chain (MOCCw) contained between 2.1% and 7.1% of the study population. This suggests a potential maximum of disease spread within this population if the disease was detected within one week. Frequency of node types within MOCCw showed considerable variability; although nursery sites were relatively most frequent. The regression analysis of several node and network level statistics indicated a potential peak time of connectivity during the summer months and warrants further confirmation and investigation. The inclusion of transportation vehicles contributed to the linear increase in the maximum weekly weak component (MWCw) size over time. This finding in combination with constant population dynamics, may have been driven by the differential utilization of trucks over time. Despite known limitations of maximum weak components as an estimator of possible outbreaks, this finding suggests that transportation vehicles should be included, when possible and relevant, in the evaluation of contacts between farms. [ABSTRACT FROM AUTHOR]

We retrospectively reviewed Elizabethkingia spp. culture and susceptibility results from 86 veterinary diagnostic laboratory results from US dogs and cats. We noted 26 E. menigoseptica, 1 E. miricola, and 59 unspeciated Elizabethkingia isolates from 9 US states (2-22 isolates per state). Elizabethkingia infections in animals might increase risks to humans. [ABSTRACT FROM AUTHOR]

Horses are a highly mobile population, with many travelling locally, nationally, and internationally to participate in shows and sporting events. However, the nature and extent of these movements, as well as the potential impact they may have on disease introduction and spread, is not well documented. The objective of this study was to characterise the movement network of a sample of horses in Ontario, Canada, over a 7-month equestrian season. Horse owners (n = 141) documented their travel patterns with their horse(s) (n = 330) by completing monthly online questionnaires between May and November 2015. Directed networks were constructed to represent horse movements in 1-month time periods. A total of 1754 horse movements met the inclusion criteria for analysis. A variety of location types were included in each monthly network, with many including non-facilities such as parks, trails, and private farms. Only 34.3% of competitions attended by participants during the study period were regulated by an official equestrian organisation. Comparisons of the similarity between monthly networks indicated that participants did not travel to the same locations each month, and the most connected locations varied between consecutive months. While the findings should not be generalized to the wider horse population, they have provided greater insight into the nature and extent of observed horse movement patterns. The results support the need to better understand the variety of locations to which horses can travel in Ontario, as different types of locations may have different associated risks of disease introduction and spread. [ABSTRACT FROM AUTHOR]

Research in big data, informatics, and bioinformatics has grown dramatically (Andreu-Perez J, et al. , 2015, IEEE Journal of Biomedical and Health Informatics 19, 1193–1208). Advances in gene sequencing technologies, surveillance systems, and electronic medical records have increased the amount of health data available. Unconventional data sources such as social media, wearable sensors, and internet search engine activity have also contributed to the influx of health data. The purpose of this study was to describe how 'big data', 'informatics', and 'bioinformatics' have been used in the animal health and veterinary medical literature and to map and chart publications using these terms through time. A scoping review methodology was used. A literature search of the terms 'big data', 'informatics', and 'bioinformatics' was conducted in the context of animal health and veterinary medicine. Relevance screening on abstract and full-text was conducted sequentially. In order for articles to be relevant, they must have used the words 'big data', 'informatics', or 'bioinformatics' in the title or abstract and full-text and have dealt with one of the major animal species encountered in veterinary medicine. Data items collected for all relevant articles included species, geographic region, first author affiliation, and journal of publication. The study level, study type, and data sources were collected for primary studies. After relevance screening, 1093 were classified. While there was a steady increase in 'bioinformatics' articles between 1995 and the end of the study period, 'informatics' articles reached their peak in 2012, then declined. The first 'big data' publication in animal health and veterinary medicine was in 2012. While few articles used the term 'big data' (n = 14), recent growth in 'big data' articles was observed. All geographic regions produced publications in 'informatics' and 'bioinformatics' while only North America, Europe, Asia, and Australia/Oceania produced publications about 'big data'. 'Bioinformatics' primary studies tended to use genetic data and tended to be conducted at the genetic level. In contrast, 'informatics' primary studies tended to use non-genetic data sources and conducted at an organismal level. The rapidly evolving definition of 'big data' may lead to avoidance of the term. [ABSTRACT FROM AUTHOR]

In the last several decades, avian influenza virus has caused numerous outbreaks around the world. These outbreaks pose a significant threat to the poultry industry and also to public health. When an avian influenza (AI) outbreak occurs, it is critical to make informed decisions about the potential risks, impact, and control measures. To this end, many modeling approaches have been proposed to acquire knowledge from different sources of data and perspectives to enhance decision making. Although some of these approaches have shown to be effective, they do not follow the process of knowledge discovery in databases (KDD). KDD is an iterative process, consisting of five steps, that aims at extracting unknown and useful information from the data. The present review attempts to survey AI modeling methods in the context of KDD process. We first divide the modeling techniques used in AI into two main categories: data-intensive modeling and small-data modeling. We then investigate the existing gaps in the literature and suggest several potential directions and techniques for future studies. Overall, this review provides insights into the control of AI in terms of the risk of introduction and spread of the virus. [ABSTRACT FROM AUTHOR]

SUMMARY: Influenza A virus in swine is of significant importance to human and veterinary public health. Environmental sampling techniques that prove practical would enhance surveillance for influenza viruses in swine. The primary objective of this study was to demonstrate the feasibility of bioaerosol and surface sampling for the detection of influenza virus in swine barns with a secondary objective of piloting a mobile application for data collection. Sampling was conducted at a large swine operation between July 2016 and August 2017. Swine oral fluids and surface swabs were collected from multiple rooms. Room‐level air samples were collected using four bioaerosol samplers: a low volume polytetrafluoroethylene (PTFE) filter sampler, the National Institute for Occupational Safety and Health's low volume cyclone sampler, a 2‐stage Andersen impactor and/or one high volume cyclonic sampler. Samples were analysed using quantitative RT‐PCR. Data and results were reported using a mobile data application. Eighty‐nine composite oral fluid samples, 70 surface swabs and 122 bioaerosol samples were analysed. Detection rates for influenza virus RNA in swine barn samples were 71.1% for oral fluids, 70.8% for surface swabs and 71.1% for the PTFE sampler. Analysis revealed a statistically significant relationship between the results of the PTFE sampler and the surface swabs with oral fluid results (p < 0.001 and p < 0.01 respectively). In addition, both the PTFE sampler (p < 0.01) and surface swabs (p = 0.03) significantly correlated with, and predicted oral fluid results. Bioaerosol sampling using PTFE samplers is an effective hands‐off approach for detecting influenza virus activity among swine. Further study is required for the implementation of this approach for surveillance and risk assessment of circulating influenza viruses of swine origin. In addition, mobile data collection stands to be an invaluable tool in the field by allowing secure, real‐time reporting of sample collection and results. [ABSTRACT FROM AUTHOR]

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Background: Middle East respiratory syndrome coronavirus (MERS-CoV) is endemic in dromedary camels in the Arabian Peninsula, and zoonotic transmission to people is a sporadic event. In the absence of epidemiological data on the reservoir species, patterns of zoonotic transmission have largely been approximated from primary human cases. This study aimed to identify meteorological factors that may increase the risk of primary MERS infections in humans.Methods: A case-crossover design was used to identify associations between primary MERS cases and preceding weather conditions within the 2-week incubation period in Saudi Arabia using univariable conditional logistic regression. Cases with symptom onset between January 2015 - December 2017 were obtained from a publicly available line list of human MERS cases maintained by the World Health Organization. The complete case dataset (N = 1191) was reduced to approximate the cases most likely to represent spillover transmission from camels (N = 446). Data from meteorological stations closest to the largest city in each province were used to calculate the daily mean, minimum, and maximum temperature (οC), relative humidity (%), wind speed (m/s), and visibility (m). Weather variables were categorized according to strata; temperature and humidity into tertiles, and visibility and wind speed into halves.Results: Lowest temperature (Odds Ratio = 1.27; 95% Confidence Interval = 1.04-1.56) and humidity (OR = 1.35; 95% CI = 1.10-1.65) were associated with increased cases 8-10 days later. High visibility was associated with an increased number of cases 7 days later (OR = 1.26; 95% CI = 1.01-1.57), while wind speed also showed statistically significant associations with cases 5-6 days later.Conclusions: Results suggest that primary MERS human cases in Saudi Arabia are more likely to occur when conditions are relatively cold and dry. This is similar to seasonal patterns that have been described for other respiratory diseases in temperate climates. It was hypothesized that low visibility would be positively associated with primary cases of MERS, however the opposite relationship was seen. This may reflect behavioural changes in different weather conditions. This analysis provides key initial evidence of an environmental component contributing to the development of primary MERS-CoV infections. [ABSTRACT FROM AUTHOR]

Middle East respiratory syndrome coronavirus (MERS‐CoV) is an emerging zoonotic pathogen discovered in 2012. The purpose of this scoping review was to summarize the empirical evidence for MERS‐CoV in animals in order to map knowledge gaps and to extract data for modelling disease transmission in dromedary camels. A review protocol was developed a priori, and a systematic search, data extraction and summary were conducted using the Arksey and O'Malley framework. Ninety‐nine publications were identified for full review out of 1,368 unique records. Of these publications, 71 were articles in scientific journals. Ninety of the studies were observational and the remaining nine were experimental. We summarize characteristics of animal studies including study design, study population and outcomes of interest for future transmission modelling in the reservoir population. The majority of field studies reported measures of prevalence, while experimental studies provided estimates of transmission parameters that pertain to the natural course of disease. [ABSTRACT FROM AUTHOR]

Background: Contact networks can be used to assess disease spread potential within a population. However, the data required to generate the networks can be challenging to collect. One method of collecting this type of data is by using radio-frequency identification (RFID) tags. The OpenBeacon RFID system generally consists of tags and readers. Communicating tags should be within 10m of the readers, which are powered by an external power source. The readers are challenging to implement in agricultural settings due to the lack of a power source and the large area needed to be covered. Methods: OpenBeacon firmware was modified to use the tag’s onboard flash memory for data storage. The tags were deployed within an equine facility for a 7-day period. Tags were attached to the horses’ halters, worn by facility staff, and placed in strategic locations around the facility to monitor which participants had contact with the specified locations during the study period. When the tags came within 2m of each other, they recorded the contact event participant IDs, and start and end times. At the end of the study period, the data were downloaded to a computer and analyzed using network analysis methods. Results: The resulting networks were plausible given the facility schedule as described in a survey completed by the facility manager. Furthermore, changes in the daily facility operations as described in the survey were reflected in the tag-collected data. In terms of the battery life, 88% of batteries maintained a charge for at least 6 days. Lastly, no consistent trends were evident in the horses’ centrality metrics. Discussion: This study demonstrates the utility of RFID tags for the collection of equine contact data. Future work should include the collection of contact data from multiple equine facilities to better characterize equine disease spread potential in Ontario. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Veterinary Research / Revue Canadienne de Recherche Vétérinaire is the property of Canadian Veterinary Medical Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Influenza A virus (IAV) in swine is a pathogen that causes a threat to the health as well as to the production of swine. Moreover, swine can spread this virus to other species including humans. The virus persists in different types of swine farms as evident in a number of studies. The core objectives of this study are (i) to analyze the dynamics of influenza infection of a farrow-to-finish swine farm, (ii) to explore the reinfection at the farm level, and finally (iii) to examine the effectiveness of two control strategies: vaccination and reduction of indirect contact. The analyses are conducted using a deterministic Susceptible-Exposed-Infectious-Recovered (SEIR) model. Simulation results show that the disease is maintained in gilts and piglets because of new susceptible pigs entering the population on a weekly basis. A sensitivity analysis shows that the results are not sensitive to variation in the parameters. The results of the reinfection simulation indicate that the virus persists in the entire farm. The control strategies studied in this work are not successful in eliminating the virus within the farm. [ABSTRACT FROM AUTHOR]

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Influenza A virus commonly circulating in swine (IAV-S) is characterized by large genetic and antigenic diversity and, thus, improvements in different aspects of IAV-S surveillance are needed to achieve desirable goals of surveillance such as to establish the capacity to forecast with the greatest accuracy the number of influenza cases likely to arise. Advancements in modeling approaches provide the opportunity to use different models for surveillance. However, in order to make improvements in surveillance, it is necessary to assess the predictive ability of such models. This study compares the sensitivity and predictive accuracy of the autoregressive integrated moving average (ARIMA) model, the generalized linear autoregressive moving average (GLARMA) model, and the random forest (RF) model with respect to the frequency of influenza A virus (IAV) in Ontario swine. Diagnostic data on IAV submissions in Ontario swine between 2007 and 2015 were obtained from the Animal Health Laboratory (University of Guelph, Guelph, ON, Canada). Each modeling approach was examined for predictive accuracy, evaluated by the root mean square error, the normalized root mean square error, and the model’s ability to anticipate increases and decreases in disease frequency. Likewise, we verified the magnitude of improvement offered by the ARIMA, GLARMA and RF models over a seasonal-naïve method. Using the diagnostic submissions, the occurrence of seasonality and the long-term trend in IAV infections were also investigated. The RF model had the smallest root mean square error in the prospective analysis and tended to predict increases in the number of diagnostic submissions and positive virological submissions at weekly and monthly intervals with a higher degree of sensitivity than the ARIMA and GLARMA models. The number of weekly positive virological submissions is significantly higher in the fall calendar season compared to the summer calendar season. Positive counts at weekly and monthly intervals demonstrated a significant increasing trend. Overall, this study shows that the RF model offers enhanced prediction ability over the ARIMA and GLARMA time series models for predicting the frequency of IAV infections in diagnostic submissions. [ABSTRACT FROM AUTHOR]

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Bovine viral diarrhea virus (BVDV) infects ruminants as primary hosts. However, other animals like pigs are susceptible. This study was conducted to investigate seroprevalence and risk factors associated with the detection of BVDV antibodies in pig herds. A total of 1.705 serum samples of 33 finisher herds, from seven Brazilian states, were collected in slaughterhouses. The samples were tested by virus neutralization (VN) test. In total, 5.35% (91/1.705) were positive and 64% (21/33) of the herds had positive animals. A significant association with “trucks are not cleaned and disinfected” and “visitors do not respect 72-h interval between visits to farms” (P < 0.05) was found in association with detection of BVDV-2 antibodies. This study suggests that important biosecurity gaps are present in Brazilian pig farms, as the presence of BVDV antibodies in pigs suggests (direct or indirect) contact with population(s) of ruminant species. Closing biosecurity gaps prevents spread of BVDV and other pathogens such as foot-and-mouth disease virus (FMDV) between pig and ruminant farms. This data should be taken in account by CSF surveillance programs, once cross-reaction in serologic tests between classical swine fever virus (CSFV) and BVDV antibodies has been shown to occur. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Veterinary Research / Revue Canadienne de Recherche Vétérinaire is the property of Canadian Veterinary Medical Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Infectious diseases in farmed animals have economic, social, and health consequences. Foreign animal diseases (FAD) of swine are of significant concern. Mathematical and simulation models are often used to simulate FAD outbreaks and best practices for control. However, simulation outcomes are sensitive to the population structure used. Within Canada, access to individual swine farm population data with which to parameterize models is a challenge because of privacy concerns. Our objective was to develop a methodology to model the farmed swine population in Ontario, Canada that could represent the existing population structure and improve the efficacy of simulation models. Results: We developed a swine population model based on the factors such as facilities supporting farm infrastructure, land availability, zoning and local regulations, and natural geographic barriers that could affect swine farming in Ontario. Assigned farm locations were equal to the swine farm density described in the 2011 Canadian Census of Agriculture. Farms were then randomly assigned to farm types proportional to the existing swine herd types. We compared the swine population models with a known database of swine farm locations in Ontario and found that the modeled population was representative of farm locations with a high accuracy (AUC: 0.91, Standard deviation: 0.02) suggesting that our algorithm generated a reasonable approximation of farm locations in Ontario. Conclusion: In the absence of a readily accessible dataset providing details of the relative locations of swine farms in Ontario, development of a model livestock population that captures key characteristics of the true population structure while protecting privacy concerns is an important methodological advancement. This methodology will be useful for individuals interested in modeling the spread of pathogens between farms across a landscape and using these models to evaluate disease control strategies. [ABSTRACT FROM AUTHOR]

Commercial production of swine often involves raising animals in large groups through the use of multi-stage production systems. In such systems, pigs can experience different degrees of contact with animals of the same or different ages. Population size and degree of contact can greatly influence transmission of endemic pathogens, including influenza A virus (IAV). IAV can display high genetic variability, which can further complicate population-level patterns. Yet, the IAV transmission in large multi-site swine production systems has not been well studied. The objectives of this study were to describe the IAV circulation in a multi-source nursery facility and identify factors associated with infection in nursery pigs. Pigs from five sow herds were mixed in one all-in/all-out nursery barn, with 81 and 75 pigs included in two longitudinal studies. Virus isolation was performed in Madin-Darby canine kidney cells and serology was performed using hemagglutination inhibition assays. Risk factor analysis for virological positivity was conducted using logistic regression and stratified Cox's regression for recurrent events. In Study 1, at ≈30 days post-weaning, 100% of pigs were positive, with 43.2% of pigs being positive recurrently over the entire study period. In study 2, 48% of pigs were positive at the peak of the outbreak and 10.7% were positive recurrently over the entire study period. The results suggest that IAV can circulate during the nursery phase in an endemic pattern and that the likelihood of recurrent infections was associated in a non-linear way with the level of heterologous (within-subtype) maternal immunity (p < 0.05). High within-pen intracluster correlation coefficients (> 0.75) were also observed for the majority of sampling times suggesting that pen-level factors played a role in infection dynamics in this study. [ABSTRACT FROM AUTHOR]

Background: Identifying the contact structure within a population of horses attending a competition is an important element towards understanding the potential for the spread of equine pathogens as the horses subsequently travel from location to location. However, there is limited information in Ontario, Canada to quantify contact patterns of horses. The objective of this study was to describe the network of potential contacts associated with an equestrian show to determine how this network structure may influence potential disease transmission. Results: This was a descriptive study of horses attending an equestrian show in southern Ontario, Canada on July 6 and 7, 2014. Horse show participants completed a questionnaire about their horse, travel patterns, and infection control practices. Questionnaire responses were received from horse owners of 79.7% (55/69) of the horses attending the show. Owners reported that horses attending the show were vaccinated for diseases such as rabies, equine influenza, and equine herpesvirus. Owners demonstrated high compliance with most infection control practices by reporting reduced opportunities for direct and indirect contact while away from home. The two-mode undirected network consisted of 820 nodes (41 locations and 779 horses). Eight percent of nodes in the network represented horses attending the show, 87% of nodes represented horses not attending the show, but boarded at individual home facilities, and 5% represented locations. The median degree of a horse in the network was 33 (range: 1-105). Conclusions: Developing disease management strategies without the explicit consideration of horses boarded at individual home facilities would underestimate the connectivity of horses in the population. The results of this study provides information that can be used by equestrian show organizers to configure event management in such a way that can limit the extent of potential disease spread. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Veterinary Journal / Revue Vétérinaire Canadienne is the property of Canadian Veterinary Medical Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Influenza A viruses (IAVs) are respiratory pathogens associated with an acute respiratory disease that occurs year-round in swine production. It is currently one of the most important pathogens in swine populations, with the potential to infect other host species including humans. Ongoing research indicates that the three major subtypes of IAV-H1N1, H1N2, and H3N2-continue to expand in their genetic and antigenic diversity. In this study, we conducted a comprehensive genomic analysis of 16 IAVs isolated from different clinical outbreaks in Alberta, Manitoba, Ontario, and Saskatchewan in 2014. We also examined the genetic basis for probable antigenic differences among sequenced viruses. On the basis of phylogenetic analysis, all 13 Canadian H3N2 viruses belonged to cluster IV, eight H3N2 viruses were part of the IV-C cluster, and one virus belonged to the IV-B and one to the IV-D cluster. Based on standards used in this study, three H3N2 viruses could not be clearly classified into any currently established group within cluster IV (A to F). Three H1N2 viruses were part of the H1 cluster. [ABSTRACT FROM AUTHOR]

The objective of this study was to develop a discrete event agent-based stochastic model to explore the likelihood of the occurrence of porcine reproductive and respiratory syndrome (PRRS) outbreaks in swine herds with different PRRS control measures in place. The control measures evaluated included vaccination with a modified-live attenuated vaccine and live-virus inoculation of gilts, and both were compared to a baseline scenario where no control measures were in place. A typical North American 1,000-sow farrow-to-wean swine herd was used as a model, with production and disease parameters estimated from the literature and expert opinion. The model constructed herein was not only able to capture individual animal heterogeneity in immunity to and shedding of the PRRS virus, but also the dynamic animal flow and contact structure typical in such herds under field conditions. The model outcomes included maximum number of females infected per simulation, and time at which that happened and the incidence of infected weaned piglets during the first year of challenge-virus introduction. Results showed that the baseline scenario produced a larger percentage of simulations resulting in outbreaks compared to the control scenarios, and interestingly some of the outbreaks occurred over long periods after virus introduction. The live-virus inoculation scenario showed promising results, with fewer simulations resulting in outbreaks than the other scenarios, but the negative impacts of maintaining a PRRS-positive population should be considered. Finally, under the assumptions of the current model, neither of the control strategies prevented the infection from spreading to the piglet population, which highlights the importance of maintaining internal biosecurity practices at the farrowing room level. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Veterinary Journal / Revue Vétérinaire Canadienne is the property of Canadian Veterinary Medical Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Veterinary Journal / Revue Vétérinaire Canadienne is the property of Canadian Veterinary Medical Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)