Within-herd transmission of Mycoplasma bovis infections after initial detection in dairy cows.

Mycoplasma bovis outbreaks in cattle, including pathogen spread between age groups, are not well understood. Our objective was to estimate within-herd transmission across adult dairy cows, youngstock, and calves. Results from 3 tests (PCR, ELISA, and culture) per cow and 2 tests (PCR and ELISA) per youngstock and calf were used in an age-stratified susceptible-infected-removed/recovered (SIR) model to estimate within-herd transmission parameters, pathways, and potential effects of farm management practices. A cohort of adult cows, youngstock, and calves on 20 Dutch dairy farms with a clinical outbreak of M. bovis in adult cows were sampled, with collection of blood, conjunctival fluid, and milk from cows, and blood and conjunctival fluid from calves and youngstock, 5 times over a time span of 12 wk. Any individual with at least one positive laboratory test was considered M. bovis-positive. Transmission dynamics were modeled using an age-stratified SIR model featuring 3 age strata. Associations with farm management practices were explored using Fisher's exact tests and Poisson regression. Estimated transmission parameters were highly variable among herds and cattle age groups. Notably, transmission from cows to cows, youngstock, or to calves was associated with R-values ranging from 1.0 to 80 secondarily infected cows per herd, 1.2 to 38 secondarily infected youngstock per herd, and 0.1 to 91 secondarily infected calves per herd, respectively. In case of transmission from youngstock to youngstock, calves or to cows, R-values were 0.7 to 96 secondarily infected youngstock per herd, 1.1 to 76 secondarily infected calves per herd, and 0.1 to 107 secondarily infected cows per herd. For transmission from calves to calves, youngstock or to cows, R-values were 0.5 to 60 secondarily infected calves per herd, 1.1 to 41 secondarily infected youngstock per herd, and 0.1 to 47 secondarily infected cows per herd. Among on-farm transmission pathways, cow-to-youngstock, cow-to-calf, and cow-to-cow were identified as most significant contributors, with calf-to-calf and calf-to-youngstock also having noteworthy roles. Youngstock-to-youngstock was also implicated, albeit to a lesser extent. Whereas the primary focus was a clinical outbreak of M. bovis among adult dairy cows, it was evident that transmission extended to calves and youngstock, contributing to overall spread. Factors influencing transmission and specific transmission pathways were associated with internal biosecurity (separate caretakers for various age groups, number of people involved), external biosecurity (contractors, external employees), as well as indirect transmission routes (number of feed and water stations).

Geographically dependent individual-level models for infectious diseases transmission.

Infectious disease models can be of great use for understanding the underlying mechanisms that influence the spread of diseases and predicting future disease progression. Modeling has been increasingly used to evaluate the potential impact of different control measures and to guide public health policy decisions. In recent years, there has been rapid progress in developing spatio-temporal modeling of infectious diseases and an example of such recent developments is the discrete-time individual-level models (ILMs). These models are well developed and provide a common framework for modeling many disease systems; however, they assume the probability of disease transmission between two individuals depends only on their spatial separation and not on their spatial locations. In cases where spatial location itself is important for understanding the spread of emerging infectious diseases and identifying their causes, it would be beneficial to incorporate the effect of spatial location in the model. In this study, we thus generalize the ILMs to a new class of geographically dependent ILMs, to allow for the evaluation of the effect of spatially varying risk factors (e.g., education, social deprivation, environmental), as well as unobserved spatial structure, upon the transmission of infectious disease. Specifically, we consider a conditional autoregressive (CAR) model to capture the effects of unobserved spatially structured latent covariates or measurement error. This results in flexible infectious disease models that can be used for formulating etiological hypotheses and identifying geographical regions of unusually high risk to formulate preventive action. The reliability of these models is investigated on a combination of simulated epidemic data and Alberta seasonal influenza outbreak data ($2009$). This new class of models is fitted to data within a Bayesian statistical framework using Markov chain Monte Carlo methods.

Latent Class Analysis for the Diagnosis of Clostridioides difficile Infection.

BACKGROUND: Clostridioides difficile infection (CDI) is an opportunistic disease that lacks a gold-standard test. Nucleic acid amplification tests such as real-time polymerase chain reaction (PCR) demonstrate an excellent limit of detection (LOD), whereas antigenic methods are able to detect protein toxin. Latent class analysis (LCA) provides an unbiased statistical approach to resolving true disease. METHODS: A cross-sectional study was conducted in patients with suspected CDI (N = 96). Four commercial real-time PCR tests, toxin antigen detection by enzyme immunoassay (EIA), toxigenic culture, and fecal calprotectin were performed. CDI clinical diagnosis was determined by consensus majority of 3 experts. LCA was performed using laboratory and clinical variables independent of any gold standard. RESULTS: Six LCA models were generated to determine CDI probability using 4 variables including toxin EIA, toxigenic culture, clinical diagnosis, and fecal calprotectin levels. Three defined zones as a function of real-time PCR cycle threshold (Ct) were identified using LCA: CDI likely (>90% probability), CDI equivocal (<90% and >10%), CDI unlikely (<10%). A single model comprising toxigenic culture, clinical diagnosis, and toxin EIA showed the best fitness. The following Ct cutoffs for 4 commercial test platforms were obtained using this model to delineate 3 CDI probability zones: GeneXpert®: 24.00, 33.61; Simplexa®: 28.97, 36.85; Elite MGB®: 30.18, 37.43; and BD Max™: 27.60, 34.26. CONCLUSIONS: The clinical implication of applying LCA to CDI is to report Ct values assigned to probability zones based on the commercial real-time PCR platform. A broad range of equivocation suggests clinical judgment is essential to the confirmation of CDI.

Effectiveness of initial methotrexate-based treatment approaches in early rheumatoid arthritis: an elicitation of rheumatologists' beliefs.

OBJECTIVES: To quantify rheumatologists' beliefs about the effectiveness of triple therapy (MTX + HCQ + SSZ) and other commonly used initial treatments for RA. METHODS: In a Bayesian belief elicitation exercise, 40 rheumatologists distributed 20 chips, each representing 5% of their total weight of belief on the probability that a typical patient with moderate-severe early RA would have an ACR50 response within 6 months with MTX (oral and s.c.), MTX + HCQ (dual therapy) and triple therapy. Parametric distributions were fit, and used to calculate pairwise median relative risks (RR), with 95% credible intervals, and estimate sample sizes for new trials to shift these beliefs. RESULTS: In the pooled analysis, triple therapy was perceived to be superior to MTX (RR 1.97; 1.35, 2.89) and dual therapy (RR 1.32; 1.03, 1.73). A pessimistic subgroup (n = 10) perceived all treatments to be similar, whereas an optimistic subgroup (n = 10) believed triple therapy to be most effective of all (RR 4.03; 2.22, 10.12). Similar variability was seen for the comparison between oral and s.c. MTX. Assuming triple therapy is truly more effective than MTX, a trial of 100 patients would be required to convince the pessimists; if triple therapy truly has no-modest effect (RR <1.5), a non-inferiority trial of 475 patients would be required to convince the optimists. CONCLUSION: Rheumatologists' beliefs regarding the effectiveness of triple therapy vary, which may partially explain the variability in its use. Owing to the strength of beliefs, some may be reluctant to shift, even with new evidence.

Spatial modeling of individual-level infectious disease transmission: Tuberculosis data in Manitoba, Canada.

Geographically dependent individual level models (GD-ILMs) are a class of statistical models that can be used to study the spread of infectious disease through a population in discrete-time in which covariates can be measured both at individual and area levels. The typical ILMs to illustrate spatial data are based on the distance between susceptible and infectious individuals. A key feature of GD-ILMs is that they take into account the spatial location of the individuals in addition to the distance between susceptible and infectious individuals. As a motivation of this article, we consider tuberculosis (TB) data which is an infectious disease which can be transmitted through individuals. It is also known that certain areas/demographics/communities have higher prevalent of TB (see Section 4 for more details). It is also of interest of policy makers to identify those areas with higher infectivity rate of TB for possible preventions. Therefore, we need to analyze this data properly to address those concerns. In this article, the expectation conditional maximization algorithm is proposed for estimating the parameters of GD-ILMs to be able to predict the areas with the highest average infectivity rates of TB. We also evaluate the performance of our proposed approach through some simulations. Our simulation results indicate that the proposed method provides reliable estimates of parameters which confirms accuracy of the infectivity rates.

Past and Future Burden of Inflammatory Bowel Diseases Based on Modeling of Population-Based Data.

BACKGROUND & AIMS: Inflammatory bowel diseases (IBDs) exist worldwide, with high prevalence in North America. IBD is complex and costly, and its increasing prevalence places a greater stress on health care systems. We aimed to determine the past current, and future prevalences of IBD in Canada. METHODS: We performed a retrospective cohort study using population-based health administrative data from Alberta (2002-2015), British Columbia (1997-2014), Manitoba (1990-2013), Nova Scotia (1996-2009), Ontario (1999-2014), Quebec (2001-2008), and Saskatchewan (1998-2016). Autoregressive integrated moving average regression was applied, and prevalence, with 95% prediction intervals (PIs), was forecasted to 2030. Average annual percentage change, with 95% confidence intervals, was assessed with log binomial regression. RESULTS: In 2018, the prevalence of IBD in Canada was estimated at 725 per 100,000 (95% PI 716-735) and annual average percent change was estimated at 2.86% (95% confidence interval 2.80%-2.92%). The prevalence in 2030 was forecasted to be 981 per 100,000 (95% PI 963-999): 159 per 100,000 (95% PI 133-185) in children, 1118 per 100,000 (95% PI 1069-1168) in adults, and 1370 per 100,000 (95% PI 1312-1429) in the elderly. In 2018, 267,983 Canadians (95% PI 264,579-271,387) were estimated to be living with IBD, which was forecasted to increase to 402,853 (95% PI 395,466-410,240) by 2030. CONCLUSION: Forecasting prevalence will allow health policy makers to develop policy that is necessary to address the challenges faced by health systems in providing high-quality and cost-effective care.

Critically important antimicrobials are generally not needed to treat nonsevere clinical mastitis in lactating dairy cows: Results from a network meta-analysis.

There is ongoing debate regarding whether critically important antimicrobials (CIA) should be used to treat infections in food-producing animals. In this systematic review, we determined whether CIA and non-CIA have comparable efficacy to treat nonsevere bovine clinical mastitis caused by the most commonly reported bacteria that cause mastitis worldwide. We screened CAB Abstracts, Web of Science, MEDLINE, Scopus, and PubMed for original epidemiological studies that assessed pathogen-specific bacteriological cure rates of antimicrobials used to treat nonsevere clinical mastitis in lactating dairy cows. Network models were fit using risk ratios of bacteriological cure as outcome. A total of 30 studies met inclusion criteria. Comparisons of cure rates demonstrated that CIA and non-CIA had comparable efficacy for treatment of nonsevere clinical mastitis in dairy cattle. Additionally, for cows with nonsevere clinical mastitis caused by Escherichia coli and Klebsiella spp., bacteriological cure rates were comparable for treated versus untreated cows; therefore, there was no evidence to justify treatment of these cases with CIA. Our findings supported that CIA in general are not necessary for treating nonsevere clinical mastitis in dairy cattle, the disease that accounts for the majority of antimicrobial usage in dairy herds worldwide. Furthermore, our findings support initiatives to reduce or eliminate use of CIA in dairy herds.

Methods for detecting seasonal influenza epidemics using a school absenteeism surveillance system.

BACKGROUND: School absenteeism data have been collected daily by the public health unit in Wellington-Dufferin-Guelph, Ontario since 2008. To date, a threshold-based approach has been implemented to raise alerts for community-wide and within-school illness outbreaks. We investigate several statistical modelling approaches to using school absenteeism for influenza surveillance at the regional level, and compare their performances using two metrics. METHODS: Daily absenteeism percentages from elementary and secondary schools, and report dates for influenza cases, were obtained from Wellington-Dufferin-Guelph Public Health. Several absenteeism data aggregations were explored, including using the average across all schools or only using schools of one type. A 10% absence threshold, exponentially weighted moving average model, logistic regression with and without seasonality terms, day of week indicators, and random intercepts for school year, and generalized estimating equations were used as epidemic detection methods for seasonal influenza. In the regression models, absenteeism data with various lags were used as predictor variables, and missing values in the datasets used for parameter estimation were handled either by deletion or linear interpolation. The epidemic detection methods were compared using a false alarm rate (FAR) as well as a metric for alarm timeliness. RESULTS: All model-based epidemic detection methods were found to decrease the FAR when compared to the 10% absence threshold. Regression models outperformed the exponentially weighted moving average model and including seasonality terms and a random intercept for school year generally resulted in fewer false alarms. The best-performing model, a seasonal logistic regression model with random intercept for school year and a day of week indicator where parameters were estimated using absenteeism data that had missing values linearly interpolated, produced a FAR of 0.299, compared to the pre-existing threshold method which at best gave a FAR of 0.827. CONCLUSIONS: School absenteeism can be a useful tool for alerting public health to upcoming influenza epidemics in Wellington-Dufferin-Guelph. Logistic regression with seasonality terms and a random intercept for school year was effective at maximizing true alarms while minimizing false alarms on historical data from this region.

Practice and attitudes regarding double gloving among staff surgeons and surgical trainees.

BACKGROUND: Despite supporting evidence, many staff surgeons and surgical trainees do not routinely double glove. We performed a study to assess rates of and attitudes toward double gloving and the use of eye protection in the operating room. METHODS: We conducted an electronic survey among all staff surgeons and surgical trainees at 2 tertiary care centres in Alberta between September and November 2015.We analyzed the data using log-binomial regression for binary outcomes to account for multiple independent variables and interactions. For 2-group comparisons, we used a 2-group test of proportions. RESULTS: The response rate was 34.3% (361/1051); 205/698 staff surgeons (29.4%) and 156/353 surgical trainees (44.2%) responded. Trainees were more likely than staff surgeons to ever double glove in the operating room (p = 0.01) and to do so routinely (p = 0.01). Staff surgeons were more likely than trainees to never double glove (p = 0.01). A total of 300/353 respondents (85.0%) reported using eye protection routinely in the operating room. Needle-stick injury was common (184 staff surgeons [92.5%], 115 trainees [74.7%]). Reduced tactile feedback, decreased manual dexterity and discomfort/poor fit were perceived barriers to double gloving. CONCLUSION: Rates of double gloving leave room for improvement. Surgical trainees were more likely than staff surgeons to double glove. Barriers remain to routine double gloving among staff surgeons and trainees. Increased education on the benefits of double gloving and early introduction of this practice may increase uptake.

CONTEXTE: Malgré les preuves à l'appui, plusieurs chirurgiens en poste et chirurgiens en formation n'utilisent pas d'emblée le double gantage. Nous avons procédé à une étude pour évaluer le taux d'utilisation du double gantage, les opinions à son endroit et l'utilisation de la protection oculaire au bloc opératoire. MÉTHODES: Nous avons envoyé un sondage électronique à tous les chirurgiens en poste et chirurgiens en formation de 2 centres de soins tertiaires de l'Alberta entre septembre et novembre 2015. Nous avons analysé les données à l'aide d'un modèle de régression logarithmique binomiale pour les résultats binaires afin de tenir compte des variables indépendantes et des interactions. Pour les comparaisons à 2 groupes, nous avons utilisé le test de comparaison de 2 proportions. RÉSULTATS: Le taux de réponse a été de 34,3 % (361/1051); 205 chirurgiens en poste sur 698 (29,4 %) et 156 chirurgiens en formation sur 353 (44,2 %) ont répondu. Au bloc opératoire, les stagiaires étaient plus susceptibles de doubler leurs gants que les chirurgiens en poste (p = 0,01) et de le faire d'emblée (p = 0,01); et les chirurgiens en poste étaient plus susceptibles de ne jamais doubler leurs gants que les stagiaires (p = 0,01). En tout 300 répondeurs sur 353 (85,0 %) ont dit utiliser d'emblée une protection oculaire au bloc opératoire. Les piqûres d'aiguille accidentelles ont été fréquentes (184 chez les chirurgiens en poste [92,5 %], 115 chez les stagiaires [74,7 %]). Une réduction de la sensibilité tactile et de la dextérité manuelle et l'inconfort ou le piètre ajustement ont été les obstacles perçus au double gantage. CONCLUSION: Les taux de double gantage laissent à désirer. Les chirurgiens en formation sont plus susceptibles d'adopter le double gantage que les chirurgiens en poste. Des obstacles continuent de nuire à l'utilisation du double gantage d'emblée, tant chez les chirurgiens en poste que chez les chirurgiens en formation. Une meilleure sensibilisation aux avantages du double gantage et l'introduction de cette pratique dès le début de la formation pourrait faciliter son adoption.

Modeling two strains of disease via aggregate-level infectivity curves.

Well formulated models of disease spread, and efficient methods to fit them to observed data, are powerful tools for aiding the surveillance and control of infectious diseases. Our project considers the problem of the simultaneous spread of two related strains of disease in a context where spatial location is the key driver of disease spread. We start our modeling work with the individual level models (ILMs) of disease transmission, and extend these models to accommodate the competing spread of the pathogens in a two-tier hierarchical population (whose levels we refer to as 'farm' and 'animal'). The postulated interference mechanism between the two strains is a period of cross-immunity following infection. We also present a framework for speeding up the computationally intensive process of fitting the ILM to data, typically done using Markov chain Monte Carlo (MCMC) in a Bayesian framework, by turning the inference into a two-stage process. First, we approximate the number of animals infected on a farm over time by infectivity curves. These curves are fit to data sampled from farms, using maximum likelihood estimation, then, conditional on the fitted curves, Bayesian MCMC inference proceeds for the remaining parameters. Finally, we use posterior predictive distributions of salient epidemic summary statistics, in order to assess the model fitted.

Variable screening methods in spatial infectious disease transmission models.

Data-driven mathematical modelling can enrich our understanding of infectious disease spread enormously. Individual-level models of infectious disease transmission allow the incorporation of different individual-level covariates, such as spatial location, vaccination status, etc. This study aims to explore and develop methods for fitting such models when we have many potential covariates to include in the model. The aim is to enhance the performance and interpretability of models and ease the computational burden of fitting these models to data. We have applied and compared multiple variable selection methods in the context of spatial epidemic data. These include a Bayesian two-stage least absolute shrinkage and selection operator (Lasso), forward and backward stepwise selection based on the Akaike information criterion (AIC), spike-and-slab priors, and random variable selection (boosting) methods. We discuss and compare the performance of these methods via simulated datasets and UK 2001 foot-and-mouth disease data. While comparing the variable selection methods all performed consistently well except the two-stage Lasso. We conclude that the spike-and-slab prior method is to be recommended, consistently resulting in high accuracy and short computational time.

Analyzing COVID-19 data in the Canadian province of Manitoba: A new approach.

The basic homogeneous SEIR (susceptible-exposed-infected-removed) model is a commonly used compartmental model for analysing infectious diseases such as influenza and COVID-19. However, in the homogeneous SEIR model, it is assumed that the population of study is homogeneous and, one cannot incorporate individual-level information (e.g., location of infected people, distance between susceptible and infected individuals, vaccination status) which may be important in predicting new disease cases. Recently, a geographically-dependent individual-level model (GD-ILM) within an SEIR framework was developed for when both regional and individual-level spatial data are available. In this paper, we propose to use an SEIR GD-ILM for each health region of Manitoba (central Canadian province) population to analyse the COVID-19 data. As different health regions of the population under study may act differently, we assume that each health region has its own corresponding parameters determined by a homogeneous SEIR model (such as contact rate, latent period, infectious period). A Monte Carlo Expectation Conditional Maximization (MCECM) algorithm is used for inference. Using estimated parameters we predict the infection rate at each health region of Manitoba over time to identify highly risk local geographical areas. Performance of the proposed approach is also evaluated through simulation studies.

Bayesian modeling of dynamic behavioral change during an epidemic.

For many infectious disease outbreaks, the at-risk population changes their behavior in response to the outbreak severity, causing the transmission dynamics to change in real-time. Behavioral change is often ignored in epidemic modeling efforts, making these models less useful than they could be. We address this by introducing a novel class of data-driven epidemic models which characterize and accurately estimate behavioral change. Our proposed model allows time-varying transmission to be captured by the level of "alarm" in the population, with alarm specified as a function of the past epidemic trajectory. We investigate the estimability of the population alarm across a wide range of scenarios, applying both parametric functions and non-parametric functions using splines and Gaussian processes. The model is set in the data-augmented Bayesian framework to allow estimation on partially observed epidemic data. The benefit and utility of the proposed approach is illustrated through applications to data from real epidemics.

Crowd-sourcing and automation facilitated the identification and classification of randomized controlled trials in a living review.

OBJECTIVES: To evaluate an approach using automation and crowdsourcing to identify and classify randomized controlled trials (RCTs) for rheumatoid arthritis (RA) in a living systematic review (LSR). METHODS: Records from a database search for RCTs in RA were screened first by machine learning and Cochrane Crowd to exclude non-RCTs, then by trainee reviewers using a Population, Intervention, Comparison, and Outcome (PICO) annotator platform to assess eligibility and classify the trial to the appropriate review. Disagreements were resolved by experts using a custom online tool. We evaluated the efficiency gains, sensitivity, accuracy, and interrater agreement (kappa scores) between reviewers. RESULTS: From 42,452 records, machine learning and Cochrane Crowd excluded 28,777 (68%), trainee reviewers excluded 4,529 (11%), and experts excluded 7,200 (17%). The 1,946 records eligible for our LSR represented 220 RCTs and included 148/149 (99.3%) of known eligible trials from prior reviews. Although excluded from our LSRs, 6,420 records were classified as other RCTs in RA to inform future reviews. False negative rates among trainees were highest for the RCT domain (12%), although only 1.1% of these were for the primary record. Kappa scores for two reviewers ranged from moderate to substantial agreement (0.40-0.69). CONCLUSION: A screening approach combining machine learning, crowdsourcing, and trainee participation substantially reduced the screening burden for expert reviewers and was highly sensitive.

Optimal reactive vaccination strategies for a foot-and-mouth outbreak in the UK.

Foot-and-mouth disease (FMD) in the UK provides an ideal opportunity to explore optimal control measures for an infectious disease. The presence of fine-scale spatio-temporal data for the 2001 epidemic has allowed the development of epidemiological models that are more accurate than those generally created for other epidemics and provide the opportunity to explore a variety of alternative control measures. Vaccination was not used during the 2001 epidemic; however, the recent DEFRA (Department for Environment Food and Rural Affairs) contingency plan details how reactive vaccination would be considered in future. Here, using the data from the 2001 epidemic, we consider the optimal deployment of limited vaccination capacity in a complex heterogeneous environment. We use a model of FMD spread to investigate the optimal deployment of reactive ring vaccination of cattle constrained by logistical resources. The predicted optimal ring size is highly dependent upon logistical constraints but is more robust to epidemiological parameters. Other ways of targeting reactive vaccination can significantly reduce the epidemic size; in particular, ignoring the order in which infections are reported and vaccinating those farms closest to any previously reported case can substantially reduce the epidemic. This strategy has the advantage that it rapidly targets new foci of infection and that determining an optimal ring size is unnecessary.

Linearized forms of individual-level models for large-scale spatial infectious disease systems.

Individual-level models (ILMs) for infectious diseases, fitted in a Bayesian MCMC framework, are an intuitive and flexible class of models that can take into account population heterogeneity via various individual-level covariates. ILMs containing a geometric distance kernel to account for geographic heterogeneity provide a natural way to model the spatial spread of many diseases. However, in even only moderately large populations, the likelihood calculations required can be prohibitively time consuming. It is possible to speed up the computation via a technique which makes use a linearized distance kernel. Here we examine some methods of carrying out this linearization and compare the performances of these methods.

Investigation of strategies for the introduction and transportation of replacement gilts on southern Ontario sow farms.

BACKGROUND: Porcine reproductive and respiratory syndrome (PRRS) is of major concern to the swine industry; infection with the virus can lead to production losses, morbidity, and mortality within swine operations. Biosecurity practices related to the management of replacement animals are important for the prevention and control of the PRRS virus, as well as other diseases. The objectives of this study were: (i) to describe individual biosecurity practices related to the introduction and transportation of replacement gilts on southern Ontario sow farms, and (ii) to understand patterns in the implementation of these practices. The second objective was accomplished using multiple correspondence analysis (MCA), which allows visualization of the relationships between individual practices and provides information about which practices frequently occur together, and which practices rarely occur together. These patterns constitute strategies for the implementation of biosecurity practices related to the introduction and transportation of replacement gilts. Data were collected using version 2 of the Production Animal Disease Risk Assessment Program's survey for the breeding herd. Two subsets of variables were retained for analysis; one subset pertained to how replacements were managed upon arrival to the farm, and the other pertained to the transportation of genetic animals. RESULTS: For both subsets of variables, the results of the MCA procedure were similar; in both solutions the 1st dimension separated herds that were closed with respect to replacement animals from herds that were open, and the 2nd dimension described how open herds managed replacements. The most interesting finding of this study was that, in some cases where a risky practice was being implemented, it was closely associated with other biosecurity practices that may mitigate that risk. CONCLUSIONS: The findings from this approach suggest that one cannot always examine biosecurity on a variable-by-variable basis. Even if a practice that is generally considered high-risk is being implemented, it may be balanced by other practices that mitigate that risk. Thus, the overall biosecurity strategy on a farm must be considered instead of only examining the implementation of individual practices.

Computationally efficient parameter estimation for spatial individual-level models of infectious disease transmission.

Individual-level models incorporate individual-specific covariate information, such as spatial location, to model infectious disease transmission. However, fitting these models with traditional Bayesian methods becomes cumbersome as model complexity or population size increases. We consider a spatial individual-level model with a binary susceptibility covariate. A method for fitting this model to aggregate-level data using traditional Metropolis-Hastings MCMC and then disaggregating the results to obtain individual-level estimates for epidemic metrics is proposed. This so-called "Cluster-Aggregate-Disaggregate" (CAD) method is compared to two approximate Bayesian computation (ABC) algorithms in a simulation study. The methods are also applied to a data set from the 2001 U.K. foot and mouth disease epidemic. While the CAD and ABC methods both performed reasonably well at capturing epidemic metrics, the CAD method was found to be much easier to implement and reduced computation time (relative to the traditional model-fitting method) more consistently than the ABC methods.

Qiviut cortisol is associated with metrics of health and other intrinsic and extrinsic factors in wild muskoxen (Ovibos moschatus).

Glucocorticoid (GC) levels are increasingly and widely used as biomarkers of hypothalamic-pituitary-adrenal (HPA) axis activity to study the effects of environmental changes and other perturbations on wildlife individuals and populations. However, identifying the intrinsic and extrinsic factors that influence GC levels is a key step in endocrinology studies to ensure accurate interpretation of GC responses. In muskoxen, qiviut (fine woolly undercoat hair) cortisol concentration is an integrative biomarker of HPA axis activity over the course of the hair's growth. We gathered data from 219 wild muskoxen harvested in the Canadian Arctic between October 2015 and May 2019. We examined the relationship between qiviut cortisol and various intrinsic (sex, age, body condition and incisor breakage) and extrinsic biotic factors (lungworm and gastrointestinal parasite infections and exposure to bacteria), as well as broader non-specific landscape and temporal features (geographical location, season and year). A Bayesian approach, which allows for the joint estimation of missing values in the data and model parameters estimates, was applied for the statistical analyses. The main findings include the following: (i) higher qiviut cortisol levels in males than in females; (ii) inter-annual variations; (iii) higher qiviut cortisol levels in a declining population compared to a stable population; (iv) a negative association between qiviut cortisol and marrow fat percentage; (v) a relationship between qiviut cortisol and the infection intensity of the lungworm Umingmakstrongylus pallikuukensis, which varied depending on the geographical location; and (vi) no association between qiviut cortisol and other pathogen exposure/infection intensity metrics. This study confirmed and further identified important sources of variability in qiviut cortisol levels, while providing important insights on the relationship between GC levels and pathogen exposure/infection intensity. Results support the use of qiviut cortisol as a tool to monitor temporal changes in HPA axis activity at a population level and to inform management and conservation actions.

Contact network uncertainty in individual level models of infectious disease transmission.

Infectious disease transmission between individuals in a heterogeneous population is often best modelled through a contact network. This contact network can be spatial in nature, with connections between individuals closer in space being more likely. However, contact network data are often unobserved. Here, we consider the fit of an individual level model containing a spatially-based contact network that is either entirely, or partially, unobserved within a Bayesian framework, using data augmented Markov chain Monte Carlo (MCMC). We also incorporate the uncertainty about event history in the disease data. We also examine the performance of the data augmented MCMC analysis in the presence or absence of contact network observational models based upon either knowledge about the degree distribution or the total number of connections in the network. We find that the latter tend to provide better estimates of the model parameters and the underlying contact network.