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Competition and indirect ELISAs are currently being used to monitor rabbit hemorrhagic disease viruses (RHDV1 and RHDV2) in rabbits worldwide. Temporal changes in the sensitivity (Se) and specificity (Sp) of assays were investigated using Bayesian Latent Class models (BCLM) in the Australian wild rabbit population where both viruses circulate simultaneously and a long-term serological dataset exists. When cELISA1 was compared to IgG1 ELISA, the Se of cELISA1 improved while the Sp of IgG1 ELISA declined over the 2011-2021. This corresponded with a decline in the true RHDV1 prevalence in 2018-21, suggesting that a large proportion of RHDV1 exposed rabbits survived the introduction and dominance of RHDV2 up to approximately 2017/2018, after which they died and were not replaced. The Se and Sp estimates for 2014-15 for both cELISA1 and IgG1 ELISA, and the true prevalence when analysing all three tests together were similar to those obtained from the analysis of cELISA1/IgG1. The same was also true for the Se and Sp of cELISA2 and IgG1 estimates from 2018 onwards. This suggests that RHDV1 was the dominant infection status in 2014-15, but RHDV2 was the dominant infection status in 2018-2021. Further, the increase in Se of cELISA2 and the low Sp of IgG1 ELISA in the cELISA2/IgG1 ELISA analysis, compared to the Se of cELISA2 and Sp of IgG1 ELISA when analysing all three tests together suggests that the underlying infection status was more influenced by RHDV2 and that the higher Se of IgG1 ELISA is due to cross-reaction of RHDV2 antibodies on IgG1 ELISA. The true prevalence data suggests that RHDV2 exposure peaked in 2017. Our findings show that test characteristics changed in response to the changing virus prevalences over time. IgG1 ELISA currently has a high Se, should be used to monitor both viruses and will perform better than both cELISAs.

The collective control programmes (CPs) that exist for many infectious diseases of farm animals rely on the application of diagnostic testing at regular time intervals for the identification of infected animals or herds. The diversity of these CPs complicates the trade of animals between regions or countries because the definition of freedom from infection differs from one CP to another. In this paper, we describe a statistical model for the prediction of herd-level probabilities of infection from longitudinal data collected as part of CPs against infectious diseases of cattle. The model was applied to data collected as part of a CP against bovine viral diarrhoea virus (BVDV) infection in Loire-Atlantique, France. The model represents infection as a herd latent status with a monthly dynamics. This latent status determines test results through test sensitivity and test specificity. The probability of becoming status positive between consecutive months is modelled as a function of risk factors (when available) using logistic regression. Modelling is performed in a Bayesian framework, using either Stan or JAGS. Prior distributions need to be provided for the sensitivities and specificities of the different tests used, for the probability of remaining status positive between months as well as for the probability of becoming positive between months. When risk factors are available, prior distributions need to be provided for the coefficients of the logistic regression, replacing the prior for the probability of becoming positive. From these prior distributions and from the longitudinal data, the model returns posterior probability distributions for being status positive for all herds on the current month. Data from the previous months are used for parameter estimation. The impact of using different prior distributions and model implementations on parameter estimation was evaluated. The main advantage of this model is its ability to predict a probability of being status positive in a month from inputs that can vary in terms of nature of test, frequency of testing and risk factor availability/presence. The main challenge in applying the model to the BVDV CP data was in identifying prior distributions, especially for test characteristics, that corresponded to the latent status of interest, i.e. herds with at least one persistently infected (PI) animal. The model is available on Github as an R package (https://github.com/AurMad/STOCfree) and can be used to carry out output-based evaluation of disease CPs. European Food Safety Authority (EFSA)

Background: Actinobacillus pleuropneumoniae is a major economically significant bacterial respiratory pathogen of pigs, and vaccine use is considered an integral control method to prevent disease. The objective of this multi-study analysis was to evaluate the serovar independent efficacy in growing pigs of the C-vaccine (Coglapix®, Ceva, France), which comprises whole cells of A. pleuropneumoniae serovars 1 and 2 expressing ApxI, ApxII and ApxIII toxins. Efficacy was based on protection against lung lesions, since there is good correlation between the severity/extension of lung lesions and losses induced by pleuropneumonia. Vaccine efficacy was determined against challenge with the most common serovars (1, 2, 4, 5, 6, 7, 9/11 and 13) of A. pleuropneumoniae in a total of 13 studies of the same design and reproducibility was validated.Results: Protection against homologous serovars 1 and 2 significantly reduced lung lesion scores (LLS) compared to the positive controls: p = 0.00007 and p = 0.00124, respectively. The protection against heterologous serovars 4, 5, 6, 7, 9/11, and 13 also significantly reduced LLS: range p = 2.9e-10 to p = 0.00953. Reproducibility between challenge studies was excellent with the estimated random effect of study (the fraction of the total variation attributed by differences between studies) being only 2.6%, 2.2% and 4.8% for three serovar 2, two serovar 9/11 and serovar 4 challenge studies, respectively. An outlier was the 35% of variation attributable to trial between the two serovar 6 challenges, possibly explained by a Streptococcus spp. outbreak.Conclusions: A highly significant serovar independent reduction of pathological lung lesions by the C-vaccine was demonstrated for all the serovars tested (1, 2, 4, 5, 6, 7, 9/11 and 13). High levels of protection with similar significance-values were obtained for both homologous and heterologous serovar challenge. To our knowledge the aerosol chamber challenge concept based on accurate individual pig dosing and a standardized biologically weighted lung lesion scoring is the first to be validated statistically as reproducible and reliable. The C-vaccine was demonstrated to be a good candidate to fulfil the demands in the field for an A. pleuropneumoniae-vaccine i.e., high protective capability against disease caused by multiple serovars.

A national screening in 2016 identified 88% of Danish pig herds positive for livestock-associated Methicillin-resistant Staphylococcus aureus (LA-MRSA). This highlights the importance of evaluating potential control measures that could reduce the prevalence of LA-MRSA among Danish pig herds. In addition to describing the effects of (1) reduced within-herd transmission, (2) increased biosecurity, and (3) movement restrictions, the eradication of LA-MRSA as a potential control measure was investigated using a simulation model mimicking the spread of LA-MRSA among pig herds between 2006 and 2015. The latter strategy was simulated either as eradication of a random selection of herds for surveillance or as a risk-based selection of herds based on their potential to spread LA-MRSA via pig movements in four different scenarios: low- vs. high-prevalence scenarios with control measures starting in 2007 and in 2010. Almost all control measures showed the potential to reduce the spread of LA-MRSA among pig herds, especially when implemented intensively and when control measures were combined. Risk-based selection of herds for eradication led to a greater relative reduction compared to random selection. In the high-prevalence scenario in particular, combinations including risk-based eradication led to the greatest relative reduction.

Estimation of the true prevalence of infected individuals involves the application of a diagnostic test to a population and adjusting according to test performance, sensitivity and specificity. Bayesian latent class analysis for the estimation of herd and animal-level true prevalence, has become increasingly used in veterinary epidemiology and is particularly useful in incorporating uncertainty and variability into analyses in a flexible framework. However, the approach has not yet been evaluated using simulated data where the true prevalence is known. Furthermore, using this approach, the within-herd true prevalence is often assumed to follow a beta distribution, the parameters of which may be modelled using hyperpriors to incorporate both uncertainty and variability associated with this parameter. Recently however, the authors of the current study highlighted a potential issue with this approach, in particular, with fitting the distributions and a tendency for the resulting distribution to invert and become clustered at zero. Therefore, the objective of the present study was to evaluate commonly specified models using simulated datasets where the herd-level true prevalence was known. The specific purpose was to compare findings from models using hyperpriors to those using a simple beta distribution to model within-herd prevalence. A second objective was to investigate sources of error by varying characteristics of the simulated dataset. Mycobacterium avium subspecies paratuberculosis infection was used as an example for the baseline dataset. Data were simulated for 1000 herds across a range of herd-level true prevalence scenarios, and models were fitted using priors from recently published studies. The results demonstrated poor performance of these latent class models for diseases characterised by poor diagnostic test sensitivity and low within-herd true prevalence. All variations of the model appeared to be sensitive to the prior and tended to overestimate herd-level true prevalence. Estimates were substantially improved in different infection scenarios by increasing test sensitivity and within-herd true prevalence. The results of this study raise questions about the accuracy of published estimates for the herd-level true prevalence of paratuberculosis based on serological testing, using latent class analysis. This study highlights the importance of conducting more rigorous sensitivity analyses than have been carried out in previous analyses published to date.

Livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) is widespread in many European countries including Denmark, where 88% of randomly selected production herds tested positive in 2016. In the present study, we investigated herd-level risk factors for farms being classified as LA-MRSA positive (study 1), in addition to herd-level risk factors for farms changing status from LA-MRSA negative to LA-MRSA positive during a 2-year period (study 2). Risk factors previously identified in other studies were confirmed in study 1: large herd size, herd type (lower risk in herds with sows) and number of pig suppliers. Due to the effect of herd type, data from sow herds (N = 41) and herds without sows (N = 166) were analysed separately. A univariable analysis found that the variables significantly associated with LA-MRSA status for sow herds were: use of wet feed in the sow units; higher weights of piglets at weaning; availability of a delivery room on the farm; cleaning of aisles after pigs were moved; number of pigs per weaner section; number of pigs purchased in the past year, and factors related to rodent control and human traffic in the herd. In herds without sows, the univariable analysis showed that the presence of other species of animal on the farm; negative pressure ventilation; full sectioning; frequent visits from the veterinarian; peroral use of tetracyclines for weaners; number of pigs purchased in the past year, and factors related to rodent control and human traffic in the herd were significantly associated with LA-MRSA status. For herds that changed from LA-MRSA negative to positive (study 2), having a company contract for mouse control, having more than one pig supplier and using group medication in the drinking water were the variables associated with LA-MRSA status in the univariable analysis. We did not succeed in building a biologically meaningful multivariable model based on any of the datasets and, as observed in similar studies, many of the risk factors identified in the univariable analysis were related to herd size. It was therefore not possible to determine whether it was the size of the herd or related factors that were the causal risk factors for being LA-MRSA positive.

Somatic cell count is frequently used as an indicator of intramammary infections (IMI) in dairy cattle worldwide. The newly introduced differential SCC (DSCC) can potentially contribute to detection of IMI. The purpose of this study was to investigate the dynamics of SCC and DSCC after IMI. We used a data set with monthly samples from 2 Danish dairy herds through 1 yr, using bacterial culture to identify IMI. The dynamics of SCC and DSCC with regard to IMI were assessed at quarter level following new IMI with each of 3 defined pathogen groups, major, minor, or "other" pathogens, using general additive models. Both SCC and DSCC increased after IMI, with a more pronounced increase if major or other pathogens were detected compared with minor pathogens. We found that DSCC increased after IMI with other pathogens in both herds and, in herd 2, after IMI caused by major and minor pathogens. We also estimated the duration of increased SCC and DSCC when they exceeded a threshold, done separately for each pathogen group. Major pathogens had the longest-lasting effect in both herds for both SCC and DSCC. We conclude that the magnitude and duration of response of SCC and DSCC to IMI differs between herds and causative pathogens.

Mastitis, often caused by intramammary infection (IMI), is a significant problem in dairy farming globally. Somatic cell count (SCC) is widely used as a parameter for screening IMI in cows that are then treated or culled. We investigated the potential of a new parameter, differential SCC (DSCC), to detect IMI at cow level when SCC is already known. We achieved this using bacterial culture (BC) and PCR to detect 4 categories of pathogens (major, minor, other, and any) in 2 Danish dairy herds. Quarter milk samples were collected from monthly dairy herd improvement samplings over 1 yr and analyzed with BC, whereas cow-level dairy herd improvement samples were analyzed using PCR. Days in milk, parity, and IMI status had a significant effect on DSCC. Using DSCC in addition to SCC significantly improved the indication of IMI compared with using only SCC in the any pathogen category in both herds as well as the minor pathogens category in herd 2 when BC was used for detection. When PCR was used to detect IMI, the use of DSCC in addition to SCC was significant for the other pathogens category in herd 1 and the minor pathogens category in herd 2. Thus, our data revealed that DSCC can add significant information describing IMI status even when SCC is already known; however, this depends on the causative pathogen. Future studies may address how to use DSCC in practice as well as consider the availability of temporal data to potentially gain insight into the course of infection.

In a sustainable production of animals, monitoring and minimizing mortality must be a top priority. Systematic measuring of mortality over time can be used to evaluate the impact of changes in management and production strategies in Norway. To aid understanding of the potential for reducing mortality, we have used data from 2014 to 2018 to investigate the spatio-temporal patterns of mortality, by descriptive analyses and statistical modelling of possible determinants of mortality. The results show large differences in mortality across different production zones and between years. The areas with the highest density of farmed salmon are also the ones with highest mortality. The total cumulated mortality of farmed salmon increased from 32.3 million in 2014 to 35.2 million in 2018, corresponding to 14.3% and 15.8% of the standing stock. An initial higher mortality was observed during the first 3 months after stocking (mean: 1.5% [0.9%-8.6%] mortality/month). This was followed by a period of stability and lower mortality (mean: 0.8% [0.9%-3.1%] mortality/month), until month 10, when mortality started to increase again. The month of first stocking, the year of slaughter, production zone and number of months at sea were all found to be statistically significant determinants for mortality, with p-values < 1e-15.

The possible spillover from pigs into other production animals incites concern for unresolved reservoirs of human exposure. The present investigation was therefore initiated, to elucidate if Danish veal and dairy farms constitute a reservoir of livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) CC398 and to potentially identify the source of introduction. We collected nasal swab samples from 17 Danish veal farms, 2 slaughterhouses, and received bulk tank milk samples from 286 dairy farms. All samples were analyzed by culturing and screening on MRSA selective plates and presumed MRSA was verified by MALDI-TOF and PCR. MRSA isolates were subjected to spa typing and whole-genome sequencing. LA-MRSA was found on two veal farms in one and three calves, respectively, with subsequent follow-up samples found negative. Eight of 286 dairy farms (2.8%) were found LA-MRSA positive and follow-up samples, from five farms showed intermittent detection of LA-MRSA. The spa types, t034 and t011, were the most common while a single isolate from a dairy farm belonged to spa type t843 associated to mecC-MRSA CC130 and is the first report of mecC-MRSA in the Danish dairy production. A phylogenetic analysis showed that some of the isolates grouped within or close to the dominant Danish pig clusters, suggesting spillover into cattle farms. Other isolates clustered outside the dominant pig clusters suggesting that other routes of introduction cannot be excluded. Results of the investigation indicated a contamination of veal farms while some dairy farms seemed to be a permanent reservoir. Thus, Danish cattle represent a low prevalence reservoir of LA-MRSA CC398, which at present, is not of major human health concern.

Streptococcus agalactiae (S. agalactiae) has re-emerged as a mastitis pathogen among Norwegian dairy cows. The Norwegian cattle health services recommend that infected herds implement measures to eradicate S. agalactiae, this includes a screening of milk samples from all lactating cows. The performance of the qPCR-test currently in use for this purpose has not been evaluated under field conditions. The objective of this study was to estimate the sensitivity and specificity of the real-time qPCR assay in use in Norway (Mastitis 4 qPCR, DNA Diagnostics A/S, Risskov, Denmark) and compare it to conventional bacteriological culturing for detection of S. agalactiae in milk samples. Because none of these tests are considered a perfect reference test, the evaluation was performed using latent class models in a Bayesian analysis. Aseptically collected cow-composite milk samples from 578 cows belonging to 6 herds were cultured and tested by qPCR. While 37 (6.4%) samples were positive for S. agalactiae by bacteriological culture, 66 (11.4%) samples were positive by qPCR. The within-herd prevalence in the six herds, as estimated by the latent class models ranged from 7.7 to 50.8%. At the recommended cut-off (cycle threshold 37), the sensitivity of the qPCR was significantly higher at 95.3 (95% posterior probability interval [PPI] [84.2; 99.6]) than that of bacteriological culture at 58.2 (95% PPI [43.8; 74.4]). However, bacterial culture had a higher specificity of 99.7 (95% PPI [98.5; 100.0]) compared to the qPCR at 98.5 (95% PPI [94.6; 99.9]). The median estimated negative predictive values of qPCR was consistently higher than those of the BC at all estimated prevalences, and the superiority of the qPCR increased with increasing within-herd prevalence. The median positive predictive values of BC was in general higher than the estimates for the qPCR, however, at the highest prevalence the predictive ability of both tests were similar.

Bacterial antimicrobial resistance (AMR) in pigs is an important public health concern due to its possible transfer to humans. We aimed at quantifying the relationship between the lifetime exposure of antimicrobials and seven antimicrobial resistance genes in Danish slaughter pig farms. AMR gene levels were quantified by qPCR of total-community DNA in faecal samples obtained from 681 batches of slaughter pigs. The lifetime exposure to antimicrobials was estimated at batch level for the piglet, weaner, and finisher periods individually for the sampled batches. We showed that the effect of antimicrobial exposure on the levels of AMR genes was complex and unique for each individual gene. Several antimicrobial classes had both negative and positive correlations with the AMR genes. From 10–42% of the variation in AMR gene levels could be explained in the final regression models, indicating that antimicrobial exposure is not the only important determinant of the AMR gene levels.

Animal and environment data are described with a multivariate dynamic linear.Observations repeatedly deviating from model forecasts indicate undesired events.Early and accurate warnings of diarrhea and pen fouling were achieved.The effect of each variable is assessed by omission and inclusion in the model. We present a method for providing early, but indiscriminant, predictions of diarrhea and pen fouling in grower/finisher pigs. We collected data on dispensed feed amount, water flow, drinking bouts frequency, temperature at two positions per pen, and section level humidity from 12 pens (6 double pens) over three full growth periods. The separate data series were co-modeled at pen level with time steps of one hour, using a multivariate dynamic linear model. The step-wise forecast errors of the model were unified using Cholesky decomposition. An alarm was raised if the unified error exceeded a set threshold a sufficient number of times, consecutively. Using this method with a 7day prediction window, we achieved an area under the receiver operating characteristics curve of 0.84. Shorter prediction windows yielded lower performances, but longer prediction windows did not affect the performance.

African swine fever (ASF) is a notifiable infectious disease with a high impact on swine health. The disease is endemic in certain regions in the Baltic countries and has spread to Poland constituting a risk of ASF spread toward Western Europe. Therefore, as part of contingency planning, it is important to explore strategies that can effectively control an epidemic of ASF. In this study, the epidemiological and economic effects of strategies to control the spread of ASF between domestic swine herds were examined using a published model (DTU-DADS-ASF). The control strategies were the basic EU and national strategy (Basic), the basic strategy plus pre-emptive depopulation of neighboring swine herds, and intensive surveillance of herds in the control zones, including testing live or dead animals. Virus spread via wild boar was not modelled. Under the basic control strategy, the median epidemic duration was predicted to be 21days (5th and 95th percentiles; 1-55days), the median number of infected herds was predicted to be 3 herds (1-8), and the total costs were predicted to be €326 million (€256-€442 million). Adding pre-emptive depopulation or intensive surveillance by testing live animals resulted in marginal improvements to the control of the epidemics. However, adding testing of dead animals in the protection and surveillance zones was predicted to be the optimal control scenario for an ASF epidemic in industrialized swine populations without contact to wild boar. This optimal scenario reduced the epidemic duration to 9days (1-38) and the total costs to €294 million (€257-€392 million). Export losses were the driving force of the total costs of the epidemics.

The aim of this study was to assess the potential of using multiple data sources currently available in Denmark for monitoring swine diseases. The study included farms that, based on serology, changed from "negative" to "positive" status for Porcine Reproductive and Respiratory Syndrome (PRRS), enzootic pneumonia (Mycoplasma hyopneumonia), and porcine pleuropneumonia (Actinobacillus pluropneumoniae) between January 2014 and September 2017. These corresponded to 45 swine farms working as individual operation units (i.e., their disease status is independent from other farms) and 81 farms that were part of joint operation units (i.e., 2 or more farms considered to be an epidemiological unit, having swine and personnel are transferred among them, that have the same disease status). Additionally, a total of 95 farms with a negative status for these three diseases during the study period were also included in the study. Changes in mortality data, antimicrobial consumption, and vaccine use at herd level were monitored using Shewhart control charts prior to, during, and after these farms were found positive for the three diseases. The analysis was run separately for the different age groups-weaners (up to 30 kg), sows and finishers herds-within each farm. Briefly, the highest percentage of herds generating alarms was generated up to 3 months before they changed their disease status based on mortality (30%) and 1 month after based on antimicrobial use for respiratory diseases (100%). Porcine pleuropneumonia showed to be the disease with the highest impact on these data at herd level; alarms based on the three data streams were generated in the same month that herds changed their status to porcine pleuropneumonia-positive, as well as the following months. Alarms based on vaccine use generally occurred within the same month or after changes in disease status. False alarms were found in 2% (median value) of the herds for the different age groups based on mortality and antimicrobial use for respiratory diseases in healthy farms. Monitoring changes in mortality data, antimicrobial consumption, and vaccine use showed changes (i.e., warnings) at herd level prior to confirmation from diagnostic tests.

There has been a rapid increase in Danish pig herds testing positive for livestock-associated Methicillin-resistant Staphylococcus aureus (LA-MRSA) since the first screening in 2008. Despite a national action plan to control LA-MRSA in the Danish pig population, 88% of pig herds tested positive in a 2016 cross-sectional study of 57 herds. The national action plan was initiated in April 2015 and aimed to reduce the spread of LA-MRSA among pig herds. However, its success is uncertain. We used a simulation model mimicking the spread of LA-MRSA among pig herds between 2006 and 2015 to evaluate the impact of control strategies if these had these been implemented in 2007 or 2010. The strategies were combinations of the following control measures: (1) a reduced number of herds using high-risk antibiotics, (2) a reduced probability of indirect transmission among herds via humans, (3) movement restrictions, and (4) voluntary eradication in 5–7.5% of the herds. Almost all tested control strategies simulated a reduction in the spread of LA-MRSA. The combination of two, three or four intervention strategies showed additive effects and led to larger reductions in the predicted herd prevalence. In addition, the prevalence of LA-MRSA-positive herds at the time when control measures were initiated influenced the effects of the control strategies. Combining the simulated control measures can be considered in future action plans to control LA-MRSA.

Vancomycin-resistant Enterococcus spp. is a major health problem worldwide and livestock have been implicated in constituting a reservoir for the transmission of vancomycin resistance to zoonotic pathogens. Vancomycin resistance determinants can be situated on mobile genetic elements and transferred between bacterial species The livestock reservoir must therefore be included in a risk assessment of the vancomycin resistance burden. Avoparcin, a vancomycin analogue, has not been used in Danish pig production for over 20 years and vancomycin has never been used. The objective of this study was to screen faecal samples from Danish pig farms for nine selected vancomycin resistance determinants. We found at least four different vancomycin resistance determinants in all screened Danish pig farms (665 finisher farms and 78 sow farms). The vancomycin resistance determinants present in vanB or vanG clusters were found at significantly different levels in sow and finisher farms. However, vanA was not detected in any of the farms. In conclusion, vancomycin resistance determinants are still present in Danish pig production 25 years after the ban on avoparcin use.

Background The modern dairy industry routinely generates data on production and disease. Therefore, the use of these cheap and at times even “free” data to predict a given state of welfare in a cost-effective manner is evaluated in the present study. Such register data could potentially be used in the identification of herds at risk of having animal welfare problems. The present study evaluated the diagnostic performance of four routinely registered indicators for identifying herds with high lameness prevalence among 40 Danish dairy herds. Indicators were extracted as within-herd annual means for a one-year period for cow mortality, bulk milk somatic cell count, proportion of lean cows at slaughter and the standard deviation (SD) of age at first calving. The target condition “high lameness prevalence” was defined as a within-herd prevalence of lame cows of ≥ 16% (third quartile). Diagnostic performance was evaluated by constructing and analysing Receiver Operating Characteristic curves and their area under the curve (AUC) for single indicators and indicator combinations. Sensitivity (Se) and specificity (Sp) of the indicators were assessed at the optimal cut-off based on data and compared to a set of predefined cut-off levels (national annual means or 90-percentile). Results Cow mortality had the highest AUC (0.76), while adding the three other indicators to the model did not yield significant increase in AUC. Cow mortality and SD of age at first calving had highest Se (100%, 95% confidence interval (CI): 72–100%), while highest Sp was found for the proportion of lean cows at slaughter (83%, 95% CI: 66–93%). The highest differential positive rate (DPR = 0.53) optimizing both Se and Sp was found for cow mortality. Optimal cut-off points were lower than the presently used pre-defined cut-offs. Conclusions The selected register-based indicators proved to be able to identify herds with high lameness prevalences. Optimized cut-offs improved the predictive ability and should therefore be preferred in official control schemes.

To gain insight into the rapid increase in the number of livestock-associated Methicillin-resistant Staphylococcus aureus (LA-MRSA)-positive herds in Denmark, we developed an individual-based Monte Carlo simulation model. We aimed to assess whether transmission of LA-MRSA via pig movements could explain the observed increase in the number of positive herds in Denmark, and to evaluate the effect of other between-herd transmission mechanisms. Pig movements alone were not sufficient to mimic the observed increase in LA-MRSA-positive herds in Denmark in any of the modelled scenarios. The model identified three factors that played important roles in the between-herd spread of LA-MRSA: (1) the within-herd dynamics, (2) the frequency and effectiveness of indirect transmissions, and (3) unexplainable introduction of LA-MRSA to swine herds. These factors can act as starting points for the development of LA-MRSA control programs in pig herds in order to limit the risk of its transmission to humans.

African swine fever (ASF) is a notifiable infectious disease with a considerable impact on animal health and is currently one of the most important emerging diseases of domestic pigs. ASF was introduced into Georgia in 2007 and subsequently spread to the Russian Federation and several Eastern European countries. Consequently, there is a non-negligible risk of ASF spread towards Western Europe. Therefore it is important to develop tools to improve our understanding of the spread and control of ASF for contingency planning. A stochastic and dynamic spatial spread model (DTU-DADS) was adjusted to simulate the spread of ASF virus between domestic swine herds exemplified by the Danish swine population. ASF was simulated to spread via animal movement, low- or medium-risk contacts and local spread. Each epidemic was initiated in a randomly selected herd – either in a nucleus herd, a sow herd, a randomly selected herd or in multiple herds simultaneously. A sensitivity analysis was conducted on input parameters. Given the inputs and assumptions of the model, epidemics of ASF in Denmark are predicted to be small, affecting about 14 herds in the worst-case scenario. The duration of an epidemic is predicted to vary from 1 to 76 days. Substantial economic damages are predicted, with median direct costs and export losses of €12 and €349 million, respectively, when epidemics were initiated in multiple herds. Each infectious herd resulted in 0 to 2 new infected herds varying from 0 to 5 new infected herds, depending on the index herd type.

High antimicrobial usage and multidrug resistance have been reported in veal calves in Europe. This may be attributed to a high risk of disease as veal calves are often purchased from numerous dairy herds, exposed to stress related to the transport and commingling of new animals, and fed a new ration. In this study, we used national register data to characterize the use of antimicrobials registered for large Danish veal calf and young bull producing herds in 2014. A total of 325 herds with veal calf and potentially young bull production were identified from the Danish Cattle database. According to the national Danish database on drugs for veterinary use (VetStat), a total of 537,399 Animal Daily Doses (ADD200) were registered for these 325 herds during 2014. The amount of antimicrobials registered in 2014 varied throughout the year, with the highest amounts registered in autumn and winter. Antimicrobials were registered for respiratory disorders (79%), joints/limbs/CNS disorders (17%), gastrointestinal disorders (3.7%) and other disorders (0.3%). Of the registered antimicrobials, 15% were for oral and 85% for parenteral administration. Long-acting formulations with a therapeutic effect of more than 48 h covered 58% of the drugs for parenteral use. Standardized at the herd-level, as ADD200/100 calves/day, antimicrobial use distributed as median [CI95%] for starter herds (n = 22): 2.14 [0.19;7.58], finisher herds (n = 24): 0.48 [0.00;1.48], full-line herds (n = 183): 0.78 [0.05;2.20] and herds with an inconsistent pattern of movements (n = 96): 0.62 [0.00;2.24]. Full-line herds are herds, which purchase calves directly from a dairy herd and raise them to slaughter. Furthermore, we performed a risk factor analysis on the 183 herds with a full-line production. Here, we investigated, whether the number of suppliers, the number of calves purchased, the frequency of purchase, the average age at introduction, the average time in the herd and vaccination influenced the amount of antimicrobials used in the herds. The final multivariable regression analysis revealed that the number of calves introduced was positively associated with the antimicrobial use in the herd.

A Danish Government PhD grant and by the University of Pretoria from the Department of Companion Animal Clinical Studies Research Fund and the Faculty of Veterinary Science Research Fund. Additional funding was provided by the Danish funds, The Danish Horse Levy Fund, and The Kustos Foundation of 1881.

The primary objective of this study was to determine the minimum number of individual fecal samples to pool together in order to obtain a representative sample for herd level quantification of antimicrobial resistance (AMR) genes in a Danish pig herd, using a novel high-throughput qPCR assay. The secondary objective was to assess the agreement between different methods of sample pooling. Quantification of AMR was achieved using a high-throughput qPCR method to quantify the levels of seven AMR genes (ermB, ermF, sulI, sulII, tet(M), tet(O) and tet(W)). A large variation in the levels of AMR genes was found between individual samples. As the number of samples in a pool increased, a decrease in sample variation was observed. It was concluded that the optimal pooling size is five samples, as an almost steady state in the variation was observed when pooling this number of samples. Good agreement between different pooling methods was found and the least time-consuming method of pooling, by transferring feces from each individual sample to a tube using a 10μl inoculation loop and adding 3.5ml of PBS, approximating a 10% solution, can therefore be used in future studies.

This study investigates how narratives and storytelling in relation to the delivery of a medical device to a patient influence the psychological effect of the device on the patient. The effect was investigated in a pilot study designed as a randomized control experiment in which an experimental group was exposed to positive narratives and storytelling in delivery of cosmetic breast enhancing surgery. The control group went through the same surgical procedures but without hearing these positive stories. Results show that the experimental group reported significantly more positive user experiences than the patients who were not exposed to the stories. It is concluded that narratives and storytelling enhance a positive psychological outcome and that more research in this area could help design the delivery of medical devices to patients.

Canine cognitive dysfunction (CCD) is a neurobehavioral syndrome occurring in some senior dogs. The diagnosis is currently primarily dependent on owner-based questionnaires addressing changes in behavior and daily routines and the exclusion of other conditions which may display clinical signs mimicking CCD. A number of CCD screening questionnaires have been published, but whether the choice of questionnaire might influence the diagnosis of CCD or not, is unclear. The objective of the present study was to correlate the total scores from 2 CCD screening questionnaires which were developed on the basis of very different strategies. The study population consisted of 50 dogs more than 8 years of age. The dogs were evaluated clinically, and the 2 questionnaires were given in a face-to-face interview with the owners. The study found a significant correlation ( r = 0.83, P

This study aimed to estimate the between- (HTP) and within- (TP) herd true prevalence distribution of Mycobacterium avium subsp. paratuberculosis (MAP) infection in dairy cattle herds participating in the Danish MAP control programme. All herds enrolled in the programme between 2011 and 2013 were included in the analysis, and one annual milk-ELISA test of all lactating cows present in such herds was considered. A Bayesian latent class model was used to obtain HTP and TP posterior distributions for each year. The model adjusts for uncertainty in age-specific test sensitivity and prior prevalence estimates. Bayesian posterior probabilities were computed in order to compare prevalence between the years. A total of 665,700 samples were included in the study, from 221,914, 224,040, and 220,466 cows sourced from 1138, 1112, and 1059 herds in years 2011, 2012, and 2013, respectively. In that period, HTP estimates of 0.92 (95% posterior probability interval (PPI), 0.87-0.96), 0.78 (95% PPI, 0.74-0.83), and 0.75 (95% PPI, 0.71-0.78) were recorded, respectively. Low TP were observed, with population mean estimates of 0.08 (95% PPI, 0.07-0.08), 0.07 (95% PPI, 0.07-0.08), and 0.07 (95% PPI, 0.06-0.07) for the three consecutive years. Statistically-important differences were recorded for HTP and population mean TP estimates between years, indicating a trend for a decreasing level of MAP infection at both herd and animal level. Model results showed that MAP infection was widespread among the Dairy cattle herds participating in the Danish control programme, though in general it was kept at very low levels.

Peel testing is used to study adhesive fracture in packaging material laminates. The focus is on improved understanding of the mechanisms that provide a laminate's adhesive properties, as measured by standard macroscopic tests. Using a specially-designed peel test load rig, peel tests are performed in-situ in a laboratory X-ray tomograph. The peel test results are analyzed using a combination of theoretical models for the adhesive fracture and 3D finite element simulations based on a cohesive zone model approach. Complementary experiments are performed to characterize the properties of the peel arm material. Relaxation of the material is found to occur during image acquisition in the in-situ tests. Despite this, it is possible to obtain 3D reconstructions with good quality during peeling. Peel test properties like the peel arm's root rotation angle and peel arm thinning are quantified. In the present 90° peel tests, it is found that the delamination progresses in an inhomogeneous manner, with the edges delaminating before the center. A number of issues and mechanisms during the peel test are identified. As an example, the peel arm itself can sometimes split, leaving residues of adhesive on the substrate surface. Such phenomena indicate the ambiguities involved in assessing adhesion properties from standard macroscopic force-displacement measurements, without accounting for the mechanisms involved on finer length scales.

Bacterial culture (BC) and quantitative polymerase chain reaction (qPCR) are widely used as diagnostic tests for pathogens causing intra-mammary infections (IMI) and it is therefore important to evaluate their performance to optimize pathogen detection. However, with no reference standard, their sensitivity (Se) and specificity (Sp) are unknown. Furthermore, the Se and Sp can differ between quarter samples and composite samples, both of which can be used to test for IMI. Latent class analysis (LCA) offers a method for estimating the Se and Sp of two tests on the same population, but to our knowledge this has not yet been applied to qPCR and BC for the detection of Staph. aureus in both composite and aseptically collected quarter milk samples. This allows for a performance evaluation of not only the two diagnostic methods, but also the two different samples –both cow and quarter level. In this study, we used Bayesian LCA on a dataset from one sampling day at a Danish dairy herd to estimate the Se and Sp for detecting Staph. aureus at cow and quarter level. We used Ct cut-offs of 32 and 37 cycles for the qPCR. When using a cut-off of 37 cycles, the estimated Se and Sp for BC were 62.2% and 90.5% at cow level, and 90.7% and 93.5% at quarter level, respectively. Similarly, the estimated Se and Sp for qPCR at cow level were 82.7% and 84.1%, respectively, and 80.0% and 96.8% at quarter level. Sp was therefore higher for both BC and qPCR at quarter level. Se was also highest for BC at quarter level, but the opposite was true for qPCR. The same pattern was found using a Ct cut-off at 32 cycles, but with different estimates. The results show that qPCR with a Ct cut-off at 37 had a higher Se than BC for composite DHI samples and it is therefore more suitable as a routine screening test for Staph. aureus. However, BC on quarter samples gave the highest Se and Sp and should therefore be used for confirmatory testing.

Precise estimates of disease transmission rates are critical for epidemiological simulation models. Most often these rates must be estimated from longitudinal field data, which are costly and time-consuming to conduct. Consequently, measures to reduce cost like increased sampling intervals or subsampling of the population are implemented. To assess the impact of such measures we implement two different SIS models to simulate disease transmission: A simple closed population model and a realistic dairy herd including population dynamics. We analyze the accuracy of different methods for estimating the transmission rate. We use data from the two simulation models and vary the sampling intervals and the size of the population sampled. We devise two new methods to determine transmission rate, and compare these to the frequently used Poisson regression method in both epidemic and endemic situations. For most tested scenarios these new methods perform similar or better than Poisson regression, especially in the case of long sampling intervals. We conclude that transmission rate estimates are easily biased, which is important to take into account when using these rates in simulation models.