Examining the effects of voluntary avoidance behaviour and policy-mediated behaviour change on the dynamics of SARS-CoV-2: A mathematical model.

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 self-protective 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.

Forecasting seasonal influenza activity in Canada-Comparing seasonal Auto-Regressive integrated moving average and artificial neural network approaches for public health preparedness.

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.

Development and validation of a farm- and province-level swine flow simulation model using discrete events and Ontario swine farm and provincial input data.

Infectious disease events can cause disruptions in service-based and agricultural industries. The list of possible events is long and varies from the incursion or emergence of a reportable animal pathogen to the recently documented interruptions caused by the COVID-19 pandemic. There is a need to develop models that can determine the impact of pathogens and mitigation measures on populations that are not directly affected by the pathogen in the case of a reportable disease, particularly when the health and welfare of these populations could be affected due to resulting disruptions in trade and supply chains. The primary objective of this study was to develop a discrete-event simulation (DES) model of swine production, including pork processing, for scenarios without major disruptions, which could be scaled from the level of an individual farm to the entire province of Ontario, Canada. The secondary objective was to validate the developed simulation against observed farm- and province-level statistics. A weekly discrete-event simulation consisting of 3 connected areas (a sow farm, a pig farm, and abattoirs) was developed using AnyLogic modelling software. Using Mann-Whitney tests, model outputs representative of the standard industry statistics were compared to data from 6 individual farms separately, as well as to provincial data from Ontario. A scalable discrete-event simulation of the swine production system for typical scenarios was accomplished. The model outputs were consistent with individual farm and industry statistics. As such, the model can be used to simulate swine production at distinct levels and could be further modified to represent swine marketing in other provinces or internationally.

Les maladies infectieuses peuvent provoquer des perturbations dans les industries de services et agricoles. La liste des événements possibles est longue et varie de l'arrivée ou de l'émergence d'un agent pathogène animal à déclaration obligatoire aux interruptions récemment documentées causées par la pandémie de COVID-19. Il est nécessaire d'élaborer des modèles permettant de déterminer l'impact des agents pathogènes et des mesures d'atténuation sur les populations qui ne sont pas directement affectées par l'agent pathogène dans le cas d'une maladie à déclaration obligatoire, en particulier lorsque la santé et le bien-être de ces populations pourraient être affectés en raison des conséquences dues aux perturbations du commerce et des chaînes d'approvisionnement. L'objectif principal de cette étude était de développer un modèle de simulation à événements discrets (DES) de la production porcine, y compris la transformation du porc, pour des scénarios sans perturbations majeures, qui pourraient être étendus du niveau d'une ferme individuelle à l'ensemble de la province de l'Ontario, Canada. L'objectif secondaire était de valider la simulation développée par rapport aux statistiques observées au niveau de la ferme et de la province. Une simulation à événements discrets hebdomadaire composée de 3 zones connectées (un élevage de truies, un élevage de porcs et des abattoirs) a été développée à l'aide du logiciel de modélisation AnyLogic. À l'aide des tests de Mann-Whitney, les résultats du modèle représentatifs des statistiques standards de l'industrie ont été comparés aux données de 6 fermes individuelles séparément, ainsi qu'aux données provinciales de l'Ontario. Une simulation à événements discrets évolutive du système de production porcine pour des scénarios typiques a été réalisée. Les résultats du modèle étaient cohérents avec les statistiques individuelles des exploitations et des industries. Ainsi, le modèle peut être utilisé pour simuler la production porcine à des niveaux distincts et pourrait être modifié davantage pour représenter la commercialisation du porc dans d'autres provinces ou à l'échelle internationale.(Traduit par Docteur Serge Messier).

Does Vaccine-Induced Maternally-Derived Immunity Protect Swine Offspring against Influenza a Viruses? A Systematic Review and Meta-Analysis of Challenge Trials from 1990 to May 2021.

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.

Development of interactive dashboards for monitoring endemic animal pathogens in Ontario, Canada: Ontario interactive animal pathogen dashboards.

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.

Epidemiology of companion animal AMR in the United States of America: filling a gap in the one health approach.

Introduction: Antimicrobial resistance (AMR) is a global health concern that affects all aspects of the One Health Triad, including human, animal, and environmental health. Companion animals, such as cats and dogs, may contribute to the spread of AMR through their close contact with humans and the frequent prescription of antimicrobials. However, research on AMR in companion animals is limited, and there are few surveillance measures in place to monitor the spread of resistant pathogens in the United States. Methods: This study aims to explore the practicality of using data from commercial laboratory antimicrobial susceptibility testing (AST) services for epidemiological analyses of AMR in companion animals in the United States. Results: The study analyzed 25,147,300 individual AST results from cats and dogs submitted to a large commercial diagnostic laboratory in the United States between 2019 and 2021, and found that resistance to certain antimicrobials was common in both E. coli and S. pseudintermedius strains. Conclusion: There has been a paucity of information regarding AMR in companion animals in comparison to human, environmental and other animal species. Commercial AST datasets may prove beneficial in providing more representation to companion animals within the One Health framework for AMR.

Classification of group A rotavirus VP7 and VP4 genotypes using random forest.

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.

Isolation of Elizabethkingia spp. from Diagnostic Specimens from Dogs and Cats, United States, 2019-2021.

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.

Classification of porcine reproductive and respiratory syndrome clinical impact in Ontario sow herds using machine learning approaches.

Since the early 1990s, porcine reproductive and respiratory syndrome (PRRS) virus outbreaks have been reported across various parts of North America, Europe, and Asia. The incursion of PRRS virus (PRRSV) in swine herds could result in various clinical manifestations, resulting in a substantial impact on the incidence of respiratory morbidity, reproductive loss, and mortality. Veterinary experts, among others, regularly analyze the PRRSV open reading frame-5 (ORF-5) for prognostic purposes to assess the risk of severe clinical outcomes. In this study, we explored if predictive modeling techniques could be used to identify the severity of typical clinical signs observed during PRRS outbreaks in sow herds. Our study aimed to evaluate four baseline machine learning (ML) algorithms: logistic regression (LR) with ridge and lasso regularization techniques, random forest (RF), k-nearest neighbor (KNN), and support vector machine (SVM), for the clinical impact classification of ORF-5 sequences and demographic data into high impact and low impact categories. First, baseline classifiers were evaluated using different input representations of ORF-5 nucleotides, amino acid sequences, and demographic data using a 10-fold cross-validation technique. Then, we designed a consensus voting ensemble approach to aggregate the different types of input representations for genetic and demographic data for classifying clinical impact. In this study, we observed that: (a) for abortion and pre-weaning mortality (PWM), different classifiers gained improvement over baseline accuracy, which showed the plausible presence of both genotypic-phenotypic and demographic-phenotypic relationships, (b) for sow mortality (SM), no baseline classifier successfully established such linkages using either genetic or demographic input data, (c) baseline classifiers showed good performance with a moderate variance of the performance metrics, due to high-class overlap and the small dataset size used for training, and (d) the use of consensus voting ensemble techniques helped to make the predictions more robust and stabilized the performance evaluation metrics, but overall accuracy did not substantially improve the diagnostic metrics over baseline classifiers.

Serum concentrations of brain-derived neurotrophic factor as a potential biomarker of swine welfare.

Objective: Describe concentrations of brain-derived neurotrophic factor (BDNF) detectable in piglet sera before and after road transport, and evaluate the correlation of serum BDNF with other physiological parameters used to assess swine welfare. Animals: Commercial crosses of piglets that underwent weaning and transport at approximately 3 wk of age. Procedure: Sixteen piglets were randomly selected from a larger study for complete blood counts, serum biochemistry testing, cortisol assays, and BDNF assays. Samples were collected 1 d before transport and immediately after transport (> 30 h) under commercial conditions. We assessed the change in serum BDNF concentration; and the correlations between serum BDNF and serum cortisol, neutrophil to lymphocyte ratios (N:L), glucose, and hematological indicators of muscle fatigue. Results: Serum BDNF concentrations increased after transport (P < 0.05) and changed inversely compared to cortisol and N:L. Consistent correlations between BDNF and other physiological parameters were not observed. High inter-pig variation in serum BDNF was present at both sample times. Conclusions: Serum BDNF may be used as an additional indicator of swine welfare. Further research characterizing piglet BDNF concentrations in response to conditions promoting positive or negative affective states would be valuable. Clinical relevance: This communication discusses common hematological parameters used to quantify changes in pig welfare and introduces BDNF, which is a parameter of interest in human cognitive functioning research that may be useful for evaluating the effect of exposure to beneficial or aversive stimuli in animals. The implications of variation in sample collection, handling, and storage procedures for BDNF detection are highlighted.

Concentrations sériques du facteur neurotrophique dérivé du cerveau en tant que biomarqueur potentiel du bien-être des porcs. Objectif: Décrire les concentrations de facteur neurotrophique dérivé du cerveau (BDNF) détectables dans les sérums de porcelets avant et après le transport routier, et évaluer la corrélation du BDNF sérique avec d'autres paramètres physiologiques utilisés pour évaluer le bien-être des porcs. Animaux: Croisements commerciaux de porcelets qui ont été sevrés et transportés à l'âge d'environ 3 semaines. Procédure: Seize porcelets ont été sélectionnés au hasard dans une étude plus vaste pour une numération globulaire complète, des tests de biochimie sérique, des dosages de cortisol et des dosages de BDNF. Les échantillons ont été prélevés 1 jour avant le transport et immédiatement après le transport (> 30 h) dans des conditions commerciales. Nous avons évalué la variation de la concentration sérique de BDNF; et les corrélations entre le BDNF sérique et le cortisol sérique, les rapports neutrophiles/lymphocytes (N:L), le glucose et les indicateurs hématologiques de la fatigue musculaire. Résultats: Les concentrations sériques de BDNF ont augmenté après le transport (P < 0,05) et ont changé inversement par rapport au cortisol et à N:L. Des corrélations cohérentes entre le BDNF et d'autres paramètres physiologiques n'ont pas été observées. Une forte variation inter-porcs du BDNF sérique était présente aux deux moments d'échantillonnage. Conclusions: Le BDNF sérique peut être utilisé comme indicateur supplémentaire du bien-être des porcs. Des recherches supplémentaires caractérisant les concentrations de BDNF chez les porcelets en réponse à des conditions favorisant des états affectifs positifs ou négatifs seraient utiles. Pertinence clinique: Cette communication traite des paramètres hématologiques courants utilisés pour quantifier les changements dans le bien-être des porcs et présente le BDNF, qui est un paramètre d'intérêt dans la recherche sur le fonctionnement cognitif humain qui peut être utile pour évaluer l'effet de l'exposition à des stimuli bénéfiques ou aversifs chez les animaux. Les implications pour la détection par le BDNF des variations dans les procédures de collecte, de manipulation et de stockage des échantillons sont mises en évidence.(Traduit par Dr Serge Messier).

Predictive analysis for pathogenicity classification of H5Nx avian influenza strains using machine learning techniques.

Over the past decades, avian influenza (AI) outbreaks have been reported across different parts of the globe, resulting in large-scale economic and livestock loss and, in some cases raising concerns about their zoonotic potential. The virulence and pathogenicity of H5Nx (e.g., H5N1, H5N2) AI strains for poultry could be inferred through various approaches, and it has been frequently performed by detecting certain pathogenicity markers in their haemagglutinin (HA) gene. The utilization of predictive modeling methods represents a possible approach to exploring this genotypic-phenotypic relationship for assisting experts in determining the pathogenicity of circulating AI viruses. Therefore, the main objective of this study was to evaluate the predictive performance of different machine learning (ML) techniques for in-silico prediction of pathogenicity of H5Nx viruses in poultry, using complete genetic sequences of the HA gene. We annotated 2137 H5Nx HA gene sequences based on the presence of the polybasic HA cleavage site (HACS) with 46.33% and 53.67% of sequences previously identified as highly pathogenic (HP) and low pathogenic (LP), respectively. We compared the performance of different ML classifiers (e.g., logistic regression (LR) with the lasso and ridge regularization, random forest (RF), K-nearest neighbor (KNN), Naïve Bayes (NB), support vector machine (SVM), and convolutional neural network (CNN)) for pathogenicity classification of raw H5Nx nucleotide and protein sequences using a 10-fold cross-validation technique. We found that different ML techniques can be successfully used for the pathogenicity classification of H5 sequences with ∼99% classification accuracy. Our results indicate that for pathogenicity classification of (1) aligned deoxyribonucleic acid (DNA) and protein sequences, with NB classifier had the lowest accuracies of 98.41% (+/-0.89) and 98.31% (+/-1.06), respectively; (2) aligned DNA and protein sequences, with LR (L1/L2), KNN, SVM (radial basis function (RBF)) and CNN classifiers had the highest accuracies of 99.20% (+/-0.54) and 99.20% (+/-0.38), respectively; (3) unaligned DNA and protein sequences, with CNN's achieved accuracies of 98.54% (+/-0.68) and 99.20% (+/-0.50), respectively. ML methods show potential for regular classification of H5Nx virus pathogenicity for poultry species, particularly when sequences containing regular markers were frequently present in the training dataset.

Morbidity of insured Swedish cats between 2011 and 2016: Comparing disease risk in domestic crosses and purebreds.

BACKGROUND: Sources of population-based cat health information are scarce. The objective of this study was to determine disease frequency in cats using pet insurance data to inform health promotion efforts. METHODS: A descriptive analysis of cats insured with Agria Pet Insurance in Sweden (2011-2016) was performed. Incidence rates of broad disease categories were calculated based on veterinary care events and an exact denominator consisting of cat-years-at-risk. Rate ratios were calculated, comparing domestic crosses to all purebreds and specific purebreds to all other purebreds combined. RESULTS: The study included over 1.6 million cat-years-at-risk (78.5% were domestic crosses), 18 breeds and 24 disease categories. The most common disease categories causing morbidity in purebreds were digestive, whole body, injury, urinary lower, skin and female reproduction. Purebreds had the highest relative risk (compared to domestics crosses) in the female reproduction, heart, operation complication, respiratory lower and immunological disease categories. LIMITATIONS: There are typical limitations of secondary data, but they do not negate the overall value of such a large dataset. CONCLUSION: This study demonstrates how pet insurance data can be used to find breed-specific differences in the incidence of various disease categories in cats. This may be of importance for breeders, cat owners, veterinarians and researchers.

Descriptive network analysis and the influence of timescale on centrality and cohesion metrics from a system of between-herd dairy cow movements in Ontario, Canada.

Previous research has demonstrated that static monthly networks of between-herd dairy cow movements in Ontario, Canada were highly fragmented, reducing potential for large-scale outbreaks. Extrapolating results from static networks can become problematic for diseases with an incubation period that exceeds the timescale of the network. The objectives of this research were to: 1) describe the networks of dairy cow movements in Ontario, and 2) describe the changes that occur among network analysis metrics when conducted at seven different timescales. Networks of dairy cow movements were created using Lactanet Canada milk recording data collected in Ontario between 2009 and 2018. Centrality and cohesion metrics were calculated after aggregating the data at seven timescales: weekly, monthly, semi-annual, annual, biennial, quinquennial, and decennial. There were 50,598 individual cows moved between Lactanet-enrolled farms, representing approximately 75% of provincially registered dairy herds. Most movements occurred over short distances (median = 39.18 km), with fewer long-range movements (maximum = 1150.80 km). The number of arcs increased marginally relative to the number of nodes with longer network timescales. Both mean out-degree, and mean clustering coefficients increased disproportionately with increasing timescale. Conversely, mean network density decreased with increasing timescale. The largest weak and strong components at the monthly timescale were small relative to the full network (267 and 4 nodes), whereas yearly networks had much higher values (2213 and 111 nodes). Higher relative connectivity in networks with longer timescales suggests pathogens with long incubation periods and animals with subclinical infection present increased potential for wide-spread disease transmission among dairy farms in Ontario. Careful consideration of disease-specific dynamics should be made when using static networks to model disease transmission among dairy cow populations.

Assessment of seasonality of rotavirus PCR detection in swine from Ontario and Quebec between 2016-2020 using submissions to a diagnostic laboratory.

The goal of this study was to determine if seasonality of rotavirus A, B, and C infection is present in Ontario and Quebec swine herds by investigating submissions to a diagnostic laboratory. Samples (N = 1557) within 755 case submissions from Canadian swine herds between 2016 and 2020 were tested for rotaviruses A, B, and C using a real-time polymerase-chain reaction assay and described. Data from Ontario and Quebec were additionally analyzed using boxplots, 6-week rolling averages, time-series decomposition, and negative binomial regression models. Percentage positivity of submissions for rotaviruses A, B, and C were discovered to be highest in nursery/weaner (n = 100, 94.0%, 60.0%, 80.0%) and grower/finisher (n = 13, 84.6%, 46.2%, 61.5%) pigs and lowest in gilt/sow (n = 45, 68.9%, 20.0%, 40.0%) and suckling pigs (n = 102, 67.6%, 10.8%, 38.2%), respectively. The most common combination of rotavirus at the sample level was AC (n = 252, 17%) and ABC (n = 175, 23.2%) at the submission level. Percent positivity for rotavirus A, B, and C across all Canadian provinces included in the study were 69.9%, 32.6%, and 53.1%, respectively. Descriptive analysis suggested little to no evidence of seasonal patterns, although a spike in November was seen in the monthly total submissions and monthly total positive submissions. Statistically, the overall month effect could not be identified as statistically significant (P > 0.05) for any of the evaluated submission counts. Overall, there was no evidence supporting seasonality of rotavirus within Ontario and Quebec swine herds between 2016 and 2020.

Le but de cette étude était de déterminer si la saisonnalité de l'infection à rotavirus A, B et C est présente dans les troupeaux de porcs de l'Ontario et du Québec en examinant les soumissions à un laboratoire de diagnostic. Des échantillons (N = 1557) de 755 cas soumis de troupeaux de porcs canadiens entre 2016 et 2020 ont été testés pour les rotavirus A, B et C à l'aide d'un test de réaction d'amplification en chaîne par polymérase en temps réel et décrits. Les données de l'Ontario et du Québec ont également été analysées à l'aide de diagrammes en boîte, de moyennes mobiles sur 6 semaines, d'une décomposition de séries chronologiques et de modèles de régression binomiale négative. On a découvert que le pourcentage de positivité des soumissions pour les rotavirus A, B et C étaient le plus élevé en pouponnière/sevrage (n = 100, 94,0 %, 60,0 %, 80,0 %) et en croissance/engraissement (n = 13, 84,6 %, 46,2 %, 61,5 %) des porcs et le plus bas chez les cochettes/truies (n = 45, 68,9 %, 20,0 %, 40,0 %) et les porcs à la mamelle (n = 102, 67,6 %, 10,8 %, 38,2 %), respectivement. La combinaison la plus courante de rotavirus au niveau de l'échantillon était AC (n = 252, 17 %) et ABC (n = 175, 23,2 %) au niveau de la soumission. Les pourcentages de positivité pour les rotavirus A, B et C dans toutes les provinces canadiennes incluses dans l'étude étaient de 69,9 %, 32,6 % et 53,1 %, respectivement. L'analyse descriptive a suggéré peu ou pas de preuves de tendances saisonnières, bien qu'un pic en novembre ait été observé dans les soumissions totales mensuelles et les soumissions positives totales mensuelles. Statistiquement, l'effet mensuel global n'a pu être identifié comme statistiquement significatif (P > 0,05) pour aucun des nombres de soumissions évalués. Dans l'ensemble, il n'y avait aucune preuve à l'appui de la saisonnalité du rotavirus dans les troupeaux de porcs de l'Ontario et du Québec entre 2016 et 2020.(Traduit par Docteur Serge Messier).

Pandemic fatigue or enduring precautionary behaviours? Canadians' long-term response to COVID-19 public health measures.

The long-term dynamics of COVID-19 disease incidence and public health measures may impact individuals' precautionary behaviours as well as support for measures. The objectives of this study were to assess longitudinal changes in precautionary behaviours and support for public health measures. Survey data were collected online from 1030 Canadians in each of 5 cycles in 2020: June 15-July 13; July 22-Aug 8; Sept 7-15; Oct 14-21; and Nov 12-17. Precautionary behaviour increased over the study period in the context of increasing disease incidence. When controlling for the stringency of public health measures and disease incidence, mixed effects logistic regression models showed these behaviours did not significantly change over time. Odds ratios for avoiding contact with family and friends ranged from 0.84 (95% CI 0.59-1.20) in September to 1.25 (95% CI 0.66-2.37) in November compared with July 2020. Odds ratios for attending an indoor gathering ranged from 0.86 (95% CI 0.62-1.20) in August to 1.71 (95% CI 0.95-3.09) in October compared with July 2020. Support for non-essential business closures increased over time with 2.33 (95% CI 1.14-4.75) times higher odds of support in November compared to July 2020. Support for school closures declined over time with lower odds of support in September (OR 0.66 [95% CI 0.45-0.96]), October (OR 0.48 [95% CI 0.26-0.87]), and November (OR 0.39 [95% CI 0.19-0.81]) compared with July 2020. In summary, respondents' behaviour mirrored government guidance between July and November 2020 and supported individual precautionary behaviour and limitations on non-essential businesses over school closures.

Network analysis of Standardbred horse movements between racetracks in Canada and the United States in 2019: Implications for disease spread and control.

Horse movements are one of the most important factors for the spread of equine diseases, and past epidemics indicate that contact networks play an important role. Network analysis was used to describe the spatial and temporal characteristics of horse movements between Standardbred racetracks in Canada and the United States during 2019, and to characterize the network to provide a better understanding of the potential racetrack-to-racetrack spread of infectious disease within the network. Networks were constructed and analyzed as an overall network (the entire study period) and monthly networks. There were 254 active Standardbred racetracks in 2019, organized in 24 geographically clustered communities. Movements and subsequent network measures of cohesiveness and centrality exhibited strong seasonal variation. Networks were more highly connected during the summer and early autumn, coinciding with peak racing activities. Monthly networks showed evidence of small-world properties, whereby disease introduction into a racetrack within a local cluster could result in the rapid spread to other racetracks within that cluster, and to other topologically distant clusters through few additional movements. Using centrality measures, a small subset of racetracks were identified as highly influential in the network and could be considered high-risk for disease introduction and disease spread to other racetracks. Enhancement of disease prevention strategies might be most appropriate if targeted to the months associated with peak racing season, and particularly to influential racetracks. The networks produced in this study were not a true representation of the entire contact network as the information contained within the race records only allowed for the consideration of between-racetrack movements. Other non-recorded movements represent further contacts in the network that can have a substantial effect on the spread of disease within a network, and the exclusion of this information can result in incorrect network measure estimates. While likely not an easy task, given the initial findings of this study and experiences from past horse industry infectious disease outbreaks, it could be beneficial for the Standardbred industry to put a movement recording strategy in place. One benefit would be enhanced ability to respond rapidly and efficiently in the event of an outbreak, thereby limiting potential animal health and economic impacts. Additional movement data could also enable further characterization of the network to inform optimal disease prevention and control strategies.

Antimicrobial use in lactating sows, piglets, nursery, and grower-finisher pigs on swine farms in Ontario, Canada during 2017 and 2018.

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.

Technology-enhanced weight-loss program in multiple-cat households: a randomized controlled trial.

OBJECTIVES: The objectives of this study were to determine whether a technology-enhanced weight-loss program, using a home pet health technology ecosystem, is an effective tool in feline weight-loss management in multiple-cat households and to evaluate its impact on cat behavior. METHODS: The study was a prospective parallel unmasked block-randomized controlled trial comparing two weight loss intervention groups: (1) traditional group with dietary restriction alone (n = 9); (2) technology group that used dietary restriction, digital scales, smart feeders, activity monitors and pet treat cameras (n = 6). A 12-week weight-loss program of client-owned indoor-only two- or three-cat households with at least one overweight cat was conducted in Canada and the USA. Owner impressions of the technology, weight loss rates, smart feeder data, activity monitor data and health-related quality of life (HRQoL) were assessed. RESULTS: The study was completed by 9/15 traditional group and 6/10 technology group cats. Dropouts were mainly due to owner issues unrelated to the study. The pet health technology ecosystem received favorable reviews (six responders). Smart feeders and home scales were perceived as valuable additions, while activity monitors and pet treat cameras were valued lower. The average weekly weight-loss rate (percent loss of initial body weight) was higher (P = 0.036) in the technology group (0.694%) than in the traditional group (0.175%). Although not associated with weight-loss rates, technology group cats trended toward grazing feeding patterns and decreased daily activity counts, while HRQoL increased, on average, for all cats. CONCLUSIONS AND RELEVANCE: This introductory investigation suggests that a technology-enhanced weight-loss program would be accepted by cat owners and may deliver advantageous outcomes in multiple-cat households, providing an effective and practical tool in feline weight-loss strategies that will continue to evolve as new technologies become available. It also illustrates the potential value of data gathered from home monitoring devices and digital diaries, providing deeper insights into pet behavior.

Serotypes, Virulence-Associated Factors, and Antimicrobial Resistance of Streptococcus suis Isolates Recovered From Sick and Healthy Pigs Determined by Whole-Genome Sequencing.

Streptococcus suis is ubiquitous in swine, and yet, only a small percentage of pigs become clinically ill. The objective of this study was to describe the distribution of serotypes, virulence-associated factor (VAF), and antimicrobial resistance (AMR) genes in S. suis isolates recovered from systemic (blood, meninges, spleen, and lymph node) and non-systemic (tonsil, nasal cavities, ileum, and rectum) sites of sick and healthy pigs using whole-genome sequencing. In total, 273 S. suis isolates recovered from 112 pigs (47 isolates from systemic and 136 from non-systemic sites of 65 sick pigs; 90 isolates from non-systemic sites of 47 healthy pigs) on 17 Ontario farms were subjected to whole-genome sequencing. Using in silico typing, 21 serotypes were identified with serotypes 9 (13.9%) and 2 (8.4%) as the most frequent serotypes, whereas 53 (19.4%) isolates remained untypable. The relative frequency of VAF genes in isolates from systemic (Kruskal-Wallis, p < 0.001) and non-systemic (Kruskal-Wallis, p < 0.001) sites in sick pigs was higher compared with isolates from non-systemic sites in healthy pigs. Although many VAF genes were abundant in all isolates, three genes, including dltA [Fisher's test (FT), p < 0.001], luxS (FT, p = 0.01), and troA (FT, p = 0.02), were more prevalent in isolates recovered from systemic sites compared with non-systemic sites of pigs. Among the isolates, 98% had at least one AMR gene, and 79% had genes associated with at least four drug classes. The most frequently detected AMR genes were tetO conferring resistance to tetracycline and ermB conferring resistance to macrolide, lincosamide, and streptogramin. The wide distribution of VAFs genes in S. suis isolates in this study suggests that other host and environmental factors may contribute to S. suis disease development.

Quantifying contact patterns in response to COVID-19 public health measures in Canada.

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.