Quantifying health risks from ESBL-producing Escherichia coli in Dutch broiler production chains and potential interventions using compartmental models.

Extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli (E. coli) in animals are considered a human health threat, because this type of bacteria can serve as a reservoir of antibiotic resistant genes and act as a continuous threat of the emergence of new resistant bacteria, in addition to the direct effect of making infection untreatable. Although the prevalence of ESBL producing bacteria in broilers was drastically reduced in the Netherlands, chicken meat still has the highest prevalence among meat products. Therefore, further control of the ESBL-producing E. coli in the broiler production chain is important to reduce public health risks. The main objectives of this study were to evaluate the effectiveness of intervention scenarios to reduce the transmission of ESBL-producing E. coli in the broiler production chain and to quantitatively estimate the risk to public health. In this study, we developed two different types of transmission models that described the observed time-related decline in prevalence during a production round: one with time-dependent decline in susceptibility and one with partial immunity to phylogenetic groups. Both models incorporated the environmental contamination effect between production rounds and within flocks. The parameter values, including transmission rate and recovery rate, were estimated by Approximate Bayesian computation (ABC) method using data from a longitudinal study in a Dutch organic broiler farm. We applied the models to the three production stages in the broiler production chain, beginning from the Parent Stock (PS) farms, the hatcheries, and to the broiler farms. In our models, eggs were collected from different parent stock farms and transported to the hatchery and from there to a broiler farm.The size of a flock and the number of farms were adjusted to the Dutch situation. Both models were able to describe the observed dynamics within and between the production stages equally well, with estimated ESBL-producing E. coli prevalence of 8.98% and 11.47% in broilers at slaughter and 0.12% and 0.15% in humans due to chicken consumption. Both models indicated that improving farm management to eliminate the bacteria from the environment was the most effective intervention, making this outcome robust. Although chicken meat consumption is not a major risk factor for human carriage of the bacteria according to our models, reducing the bacteria in the PS and broiler farm environment to at least one percent can further decrease the prevalence in humans.

Understanding the interface between European wild boar (Sus scrofa) and domestic pigs (Sus scrofa domesticus) in Sweden through a questionnaire study.

BACKGROUND: In recent years, the wildlife/livestock interface has attracted increased attention due to disease transmission between wild and domestic animal populations. The ongoing spread of African swine fever (ASF) in European wild boar (Sus scrofa) emphasize the need for further understanding of the wildlife/livestock interface to prevent disease spill-over between the wild and domestic populations. Although wild boar may also act as a potential source for other infectious disease agents, ASF is currently the most severe threat from wild boar to domestic pigs. To gather information on the wild boar situation at commercial pig producing farms in Sweden, a digital questionnaire survey was distributed through the animal health services. RESULTS: Most pigs produced for commercial purposes in Sweden are raised without outdoor access. Of the 211 responding pig producers, 80% saw wild boar or signs of wild boar activity in the vicinity of their farm at least once during the year. Observations were significantly correlated with geographical region, but there was no correlation between farm characteristics (farm size, main type of production, outdoor access) and observed wild boar presence or proximity. However, a reported higher frequency of wild boar observations was positively correlated with the observations being made in closer proximity to the farm. Hunting and strategic baiting were the most common mitigation strategies used to keep wild boar at bay. Of the 14 farms raising pigs with outdoor access, 12 responded that these pigs could be raised solely indoors if needed. Pigs with outdoor access are required to be fenced in, but double fencing in these outdoor pig enclosures was not practiced by all. A perimeter fence surrounding any type of pig farm was very rare. More than half of the producers that grew crops with intended use for pigs reported crop damage by wild boar. CONCLUSION: This study shows that although pigs raised for commercial purposes in Sweden are, to a large extent, kept indoors the potential for indirect contact with wild boar exists and must be considered. Variable local situations regarding wild boar abundance may require an adaptive approach regarding biosecurity efforts.

Data workflows and visualization in support of surveillance practice.

The Swedish National Veterinary Institute (SVA) is working on implementing reusable and adaptable workflows for epidemiological analysis and dynamic report generation to improve disease surveillance. Important components of this work include: data access, development environment, computational resources and cloud-based management. The development environment relies on Git for code collaboration and version control and the R language for statistical computing and data visualization. The computational resources include both local and cloud-based systems, with automatic workflows managed in the cloud. The workflows are designed to be flexible and adaptable to changing data sources and stakeholder demands, with the ultimate goal to create a robust infrastructure for the delivery of actionable epidemiological information.

Complex network analysis to understand trading partnership in French swine production.

The circulation of livestock pathogens in the pig industry is strongly related to animal movements. Epidemiological models developed to understand the circulation of pathogens within the industry should include the probability of transmission via between-farm contacts. The pig industry presents a structured network in time and space, whose composition changes over time. Therefore, to improve the predictive capabilities of epidemiological models, it is important to identify the drivers of farmers' choices in terms of trade partnerships. Combining complex network analysis approaches and exponential random graph models, this study aims to analyze patterns of the swine industry network and identify key factors responsible for between-farm contacts at the French scale. The analysis confirms the topological stability of the network over time while highlighting the important roles of companies, types of farm, farm sizes, outdoor housing systems and batch-rearing systems. Both approaches revealed to be complementary and very effective to understand the drivers of the network. Results of this study are promising for future developments of epidemiological models for livestock diseases. This study is part of the One Health European Joint Programme: BIOPIGEE.

Modelling spread and surveillance of Mycobacterium avium subsp. paratuberculosis in the Swedish cattle trade network.

To monitor a state of disease freedom and to ensure a timely detection of new introductions of disease, surveillance programmes need be evaluated prior to implementation. We present a strategy to evaluate surveillance of Mycobacterium avium subsp. paratuberculosis (MAP) using simulated testing of bulk milk in an infectious disease spread model. MAP is a globally distributed, chronic infectious disease with substantial animal health impact. Designing surveillance for this disease poses specific challenges because methods for surveillance evaluation have focused on estimating surveillance system sensitivity and probability of freedom from disease and do not account for spread of disease or complex and changing population structure over long periods. The aims of the study were to 1. define a model that describes the spread of MAP within and between Swedish herds; 2. define a method for simulation of imperfect diagnostic testing in this framework; 3. to compare surveillance strategies to support surveillance design choices. The results illustrate how this approach can be used to identify differences between the probability of detecting disease in the population based on choices of the number of herds sampled and the use of risk-based or random selection of these herds. The approach was also used to assess surveillance to detect introduction of disease and to detect a very low prevalence endemic state. The use of bulk milk sampling was determined to be an effective method to detect MAP in the population with as few as 500 herds tested per year if the herd-level prevalence was 0.2 %. However, detection of point introductions in the population was unlikely in the 13-year simulation period even if as many as 2000 herds were tested per year. Interestingly, the use of a risk-based selection strategy was found to be a disadvantage to detect MAP given the modelled disease dynamics.

Bayesian epidemiological modeling over high-resolution network data.

Mathematical epidemiological models have a broad use, including both qualitative and quantitative applications. With the increasing availability of data, large-scale quantitative disease spread models can nowadays be formulated. Such models have a great potential, e.g., in risk assessments in public health. Their main challenge is model parameterization given surveillance data, a problem which often limits their practical usage. We offer a solution to this problem by developing a Bayesian methodology suitable to epidemiological models driven by network data. The greatest difficulty in obtaining a concentrated parameter posterior is the quality of surveillance data; disease measurements are often scarce and carry little information about the parameters. The often overlooked problem of the model's identifiability therefore needs to be addressed, and we do so using a hierarchy of increasingly realistic known truth experiments. Our proposed Bayesian approach performs convincingly across all our synthetic tests. From pathogen measurements of shiga toxin-producing Escherichia coli O157 in Swedish cattle, we are able to produce an accurate statistical model of first-principles confronted with data. Within this model we explore the potential of a Bayesian public health framework by assessing the efficiency of disease detection and -intervention scenarios.

A between-herd data-driven stochastic model to explore the spatio-temporal spread of hepatitis E virus in the French pig production network.

Hepatitis E virus is a zoonotic pathogen for which pigs are recognized as the major reservoir in industrialised countries. A multiscale model was developed to assess the HEV transmission and persistence pattern in the pig production sector through an integrative approach taking into account within-farm dynamics and animal movements based on actual data. Within-farm dynamics included both demographic and epidemiological processes. Direct contact and environmental transmission routes were considered along with the possible co-infection with immunomodulating viruses (IMVs) known to modify HEV infection dynamics. Movements were limited to 3,017 herds forming the largest community on the swine commercial network in France and data from the national pig movement database were used to build the contact matrix. Between-herd transmission was modelled by coupling within-herd and network dynamics using the SimInf package. Different introduction scenarios were tested as well as a decrease in the prevalence of IMV-infected farms. After introduction of a single infected gilt, the model showed that the transmission pathway as well as the prevalence of HEV-infected pigs at slaughter age were affected by the type of the index farm, the health status of the population and the type of the infected farms. These outcomes could help design HEV control strategies at a territorial scale based on the assessment of the farms' and network's risk.

Spatio-temporal modelling of verotoxigenic Escherichia coli O157 in cattle in Sweden: exploring options for control.

A spatial data-driven stochastic model was developed to explore the spread of verotoxigenic Escherichia coli O157 (VTEC O157) by livestock movements and local transmission among neighbouring holdings in the complete Swedish cattle population. Livestock data were incorporated to model the time-varying contact network between holdings and population demographics. Furthermore, meteorological data with the average temperature at the geographical location of each holding was used to incorporate season. The model was fitted against observed data and extensive numerical experiments were conducted to investigate the model's response to control strategies aimed at reducing shedding and susceptibility, as well as interventions informed by network measures. The results showed that including local spread and season improved agreement with prevalence studies. Also, control strategies aimed at reducing the average shedding rate were more efficient in reducing the VTEC O157 prevalence than strategies based on network measures. The methodology presented in this study could provide a basis for developing disease surveillance on regional and national scales, where observed data are combined with readily available high-resolution data in simulations to get an overview of potential disease spread in unobserved regions.

Mechanistic modelling of interventions against spread of livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) within a Danish farrow-to-finish pig herd.

Knowledge on successful interventions against livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) within pig herds is sparse. In situations like this, a mechanistic simulation model can be a valuable tool for assessing the effect of potential intervention strategies, and prioritising which should be tested in the field. We have simulated on-farm interventions in a farrow-to-finish pig herd, with a previously published LA-MRSA spread model, within four different areas: 1) Reduced antimicrobial consumption, 2) Reduced number of pigs within each section, 3) Reduced mixing of pigs, and 4) Improved internal biosecurity. To model a decrease in the selective pressure, the transmission rates were reduced after LA-MRSA had become fully established within a herd, which resulted in a marked decrease in the prevalence within all stable units. However, LA-MRSA rarely disappeared completely from the herd; this was only observed in scenarios where the transmission rates were reduced to ≤ 30% of the original level. While changes in antimicrobial consumption patterns might be a very important step towards reducing the spread of LA-MRSA, the simulation results indicate that it may need to be paired with other preventive or intervention measures. Reducing the number of pigs within each section, reducing mixing of pigs, or improving internal biosecurity after LA-MRSA had become established within the herd only resulted in marginal changes in the median prevalence within the herd. However, these factors might be important in relation to being able to achieve or maintain a low level of antimicrobial consumption, and thus still indirectly influence the LA-MRSA prevalence within the herd. The results of a sensitivity analysis indicated the assumptions regarding the existence of pigs persistently shedding MRSA have a noticeable influence on the model results. The assumptions regarding transmission from sow to offspring at the day of birth also had a considerable influence on the MRSA prevalence within the farrowing unit but did not cause any marked changes in the simulated effect of interventions. Effects might differ between different farm types contaminated in different levels and this simulation study highlights a strong need for more knowledge from on-farm trials.

Evaluation of Strategies to Control a Potential Outbreak of Foot-and-Mouth Disease in Sweden.

To minimize the potential consequences of an introduction of foot-and-mouth disease (FMD) in Europe, European Union (EU) member states are required to present a contingency plan. This study used a simulation model to study potential outbreak scenarios in Sweden and evaluate the best control strategies. The model was informed by the Swedish livestock structure using herd information from cattle, pig, and small ruminant holdings in the country. The contact structure was based on animal movement data and studies investigating the movements between farms of veterinarians, service trucks, and other farm visitors. All scenarios of outbreak control included depopulation of detected herds, 3 km protection and 10 km surveillance zones, movement tracing, and 3 days national standstill. The effect of availability of surveillance resources, i.e., number of field veterinarians per day, and timeliness of enforcement of interventions, was assessed. With the estimated currently available resources, an FMD outbreak in Sweden is expected to be controlled (i.e., last infected herd detected) within 3 weeks of detection in any evaluated scenario. The density of farms in the area where the epidemic started would have little impact on the time to control the outbreak, but spread in high density areas would require more surveillance resources, compared to areas of lower farm density. The use of vaccination did not result in a reduction in the expected number of infected herds. Preemptive depopulation was able to reduce the number of infected herds in extreme scenarios designed to test a combination of worst-case conditions of virus introduction and spread, but at the cost of doubling the number of herds culled. This likely resulted from a combination of the small outbreaks predicted by the spread model, and the high efficacy of the basic control measures evaluated, under the conditions of the Swedish livestock industry, and considering the assumed control resources available. The results indicate that the duration and extent of FMD outbreaks could be kept limited in Sweden using the EU standard control strategy and a 3 days national standstill.

Data-driven network modelling of disease transmission using complete population movement data: spread of VTEC O157 in Swedish cattle.

European Union legislation requires member states to keep national databases of all bovine animals. This allows for disease spread models that includes the time-varying contact network and population demographic. However, performing data-driven simulations with a high degree of detail are computationally challenging. We have developed an efficient and flexible discrete-event simulator SimInf for stochastic disease spread modelling that divides work among multiple processors to accelerate the computations. The model integrates disease dynamics as continuous-time Markov chains and livestock data as events. In this study, all Swedish livestock data (births, movements and slaughter) from July 1st 2005 to December 31st 2013 were included in the simulations. Verotoxigenic Escherichia coli O157:H7 (VTEC O157) are capable of causing serious illness in humans. Cattle are considered to be the main reservoir of the bacteria. A better understanding of the epidemiology in the cattle population is necessary to be able to design and deploy targeted measures to reduce the VTEC O157 prevalence and, subsequently, human exposure. To explore the spread of VTEC O157 in the entire Swedish cattle population during the period under study, a within- and between-herd disease spread model was used. Real livestock data was incorporated to model demographics of the population. Cattle were moved between herds according to real movement data. The results showed that the spatial pattern in prevalence may be due to regional differences in livestock movements. However, the movements, births and slaughter of cattle could not explain the temporal pattern of VTEC O157 prevalence in cattle, despite their inherently distinct seasonality.

Longitudinal observational study over 38 months of verotoxigenic Escherichia coli O157:H7 status in 126 cattle herds.

Verotoxigenic Escherichia coli O157:H7 (VTEC O157:H7) is an important zoonotic pathogen capable of causing infections in humans, sometimes with severe symptoms such as hemorrhagic colitis and hemolytic uremic syndrome (HUS). It has been reported that a subgroup of VTEC O157:H7, referred to as clade 8, is overrepresented among HUS cases. Cattle are considered to be the main reservoir of VTEC O157:H7 and infected animals shed the bacteria in feces without showing clinical signs of disease. The aims of the present study were: (1) to better understand how the presence of VTEC O157:H7 in the farm environment changes over an extended period of time, (2) to investigate potential risk factors for the presence of the bacteria, and (3) describe the distribution of MLVA types and specifically the occurrence of the hypervirulent strains (clade 8 strains) of VTEC O157:H7. The farm environment of 126 cattle herds in Sweden were sampled from October 2009 to December 2012 (38 months) using pooled pat and overshoe sampling. Each herd was sampled, on average, on 17 occasions (range=1-20; median=19), at intervals of 64 days (range=7-205; median=58). Verotoxigenic E. coli O157:H7 were detected on one or more occasions in 53% of the herds (n=67). In these herds, the percentage of positive sampling occasions ranged from 6% to 72% (mean=19%; median=17%). Multi-locus variable number tandem repeat analysis (MLVA) typing was performed on isolates from infected herds to identify hypervirulent strains (clade 8). Clustering of MLVA profiles yielded 35 clusters and hypervirulent strains were found in 18 herds; the same cluster was often identified on consecutive samplings and in nearby farms. Using generalized estimating equations, an association was found between the probability of detecting VTEC O157:H7 and status at the preceding sampling, season, herd size, infected neighboring farms and recent introduction of animals. This study showed that the bacteria VTEC O157:H7 were spontaneously cleared from the farm environment in most infected herds over time, and key factors were identified to prevent the spread of VTEC O157:H7 between cattle herds.

Evaluation of farm-level parameters derived from animal movements for use in risk-based surveillance programmes of cattle in Switzerland.

BACKGROUND: This study focused on the descriptive analysis of cattle movements and farm-level parameters derived from cattle movements, which are considered to be generically suitable for risk-based surveillance systems in Switzerland for diseases where animal movements constitute an important risk pathway. METHODS: A framework was developed to select farms for surveillance based on a risk score summarizing 5 parameters. The proposed framework was validated using data from the bovine viral diarrhoea (BVD) surveillance programme in 2013. RESULTS: A cumulative score was calculated per farm, including the following parameters; the maximum monthly ingoing contact chain (in 2012), the average number of animals per incoming movement, use of mixed alpine pastures and the number of weeks in 2012 a farm had movements registered. The final score for the farm depended on the distribution of the parameters. Different cut offs; 50, 90, 95 and 99%, were explored. The final scores ranged between 0 and 5. Validation of the scores against results from the BVD surveillance programme 2013 gave promising results for setting the cut off for each of the five selected farm level criteria at the 50th percentile. Restricting testing to farms with a score ≥ 2 would have resulted in the same number of detected BVD positive farms as testing all farms, i.e., the outcome of the 2013 surveillance programme could have been reached with a smaller survey. CONCLUSIONS: The seasonality and time dependency of the activity of single farms in the networks requires a careful assessment of the actual time period included to determine farm level criteria. However, selecting farms in the sample for risk-based surveillance can be optimized with the proposed scoring system. The system was validated using data from the BVD eradication program. The proposed method is a promising framework for the selection of farms according to the risk of infection based on animal movements.

A novel method to identify herds with an increased probability of disease introduction due to animal trade.

In the design of surveillance, there is often a desire to target high risk herds. Such risk-based approaches result in better allocation of resources and improve the performance of surveillance activities. For many contagious animal diseases, movement of live animals is a main route of transmission, and because of this, herds that purchase many live animals or have a large contact network due to trade can be seen as a high risk stratum of the population. This paper presents a new method to assess herd disease risk in animal movement networks. It is an improvement to current network measures that takes direction, temporal order, and also movement size and probability of disease into account. In the study, the method was used to calculate a probability of disease ratio (PDR) of herds in simulated datasets, and of real herds based on animal movement data from dairy herds included in a bulk milk survey for Coxiella burnetii. Known differences in probability of disease are easily incorporated in the calculations and the PDR was calculated while accounting for regional differences in probability of disease, and also by applying equal probability of disease throughout the population. Each herd's increased probability of disease due to purchase of animals was compared to both the average herd and herds within the same risk stratum. The results show that the PDR is able to capture the different circumstances related to disease prevalence and animal trade contact patterns. Comparison of results based on inclusion or exclusion of differences in risk also highlights how ignoring such differences can influence the ability to correctly identify high risk herds. The method shows a potential to be useful for risk-based surveillance, in the classification of herds in control programmes or to represent influential contacts in risk factor studies.

Relative contributions of neighbourhood and animal movements to Coxiella burnetii infection in dairy cattle herds.

Q fever in dairy cattle herds occurs mainly after inhalation of contaminated aerosols generated from excreta by shedder animals. Propagation of Coxiella burnetii, the cause of the disease between ruminant herds could result from transmission between neighbouring herds and/or the introduction of infected shedder animals in healthy herds. The objective of this study were (i) to describe the spatial distribution C. burnetii-infected dairy cattle herds in two different regions: the Finistère District in France (2,829 herds) and the island of Gotland in Sweden (119 herds) and (ii) to quantify and compare the relative contributions of C. burnetii transmission related to neighbourhood and to animal movements on the risk for a herd to be infected. An enzyme--linked immunosorbent assay was used for testing bulk tank milk in May 2012 and June 2011, respectively. Only one geographical cluster of positive herds was identified in north-western Finistère. Logistic regression was used to assess the association of risk for a herd to test positively with local cattle density (the total number of cattle located in a 5 km radius circle) and the in-degree (ID) parameter, a measure of the number of herds from which each herd had received animals directly within the last 2 years. The risk for a herd to test positively was higher for herds with a higher local cattle density [odds ratio (OR) = 2.3, 95% confidence interval (CI) = 1.6-3.2, for herds with a local density between 100 and 120 compared to herds with a local density 60]. The risk was also higher for herds with higher IDs (OR = 2.3, 95% CI = 1.6-3.2, for herds with ID 3 compared to herds that did not introduce animals). The proportion of cases attributable to infections in the neighbourhood in high-density areas was twice the proportion attributable to animal movements, suggesting that wind plays a main role in the transmission.

EpiContactTrace: an R-package for contact tracing during livestock disease outbreaks and for risk-based surveillance.

BACKGROUND: During outbreak of livestock diseases, contact tracing can be an important part of disease control. Animal movements can also be of relevance for risk-based surveillance and sampling, i.e. both when assessing consequences of introduction or likelihood of introduction. In many countries, animal movement data are collected with one of the major objectives to enable contact tracing. However, often an analytical step is needed to retrieve appropriate information for contact tracing or surveillance. RESULTS: In this study, an open source tool was developed to structure livestock movement data to facilitate contact-tracing in real time during disease outbreaks and for input in risk-based surveillance and sampling. The tool, EpiContactTrace, was written in the R-language and uses the network parameters in-degree, out-degree, ingoing contact chain and outgoing contact chain (also called infection chain), which are relevant for forward and backward tracing respectively. The time-frames for backward and forward tracing can be specified independently and search can be done on one farm at a time or for all farms within the dataset. Different outputs are available; datasets with network measures, contacts visualised in a map and automatically generated reports for each farm either in HTML or PDF-format intended for the end-users, i.e. the veterinary authorities, regional disease control officers and field-veterinarians. EpiContactTrace is available as an R-package at the R-project website (http://cran.r-project.org/web/packages/EpiContactTrace/). CONCLUSIONS: We believe this tool can help in disease control since it rapidly can structure essential contact information from large datasets. The reproducible reports make this tool robust and independent of manual compilation of data. The open source makes it accessible and easily adaptable for different needs.

Environmental sampling for evaluating verotoxigenic Escherichia coli O157: H7 status in dairy cattle herds.

Verotoxigenic Escherichia coli O157:H7 is a zoonotic bacterial pathogen capable of causing severe disease in human beings. Cattle are considered to be the main reservoir of the bacterium. The objective of the current study was to compare environmental sampling (consisting of dust, overshoe, and pooled pat samples) with pooled, individual fecal sampling for determining the cattle herd status under field conditions in naturally infected dairy herds. Thirty-one dairy cattle farms in Sweden, where verotoxigenic E. coli O157:H7 had been previously detected, were visited. On each farm, dust, overshoe, and pooled pat sampling were performed in each of 3 different age categories: calves, young stock, and adults. In addition, up to 140 individual fecal samples were collected and analyzed as pooled samples. In total, 3,763 individual fecal and 270 environmental samples were collected and analyzed for the presence of verotoxigenic E. coli O157:H7. Overshoe sampling, alone or in combination with dust and pooled pat sampling, correctly classified 20 of the 24 (0.83, 95% CI: 0.63-0.95) herds detected with at least 1 positive pool. On 1 farm, a dust sample was positive although all other samples were negative. In 6 of the 31 farms, the bacteria could not be detected in any of the individual fecal samples or in the environmental samples. The results establish that environmental sampling is a reliable method for identifying cattle herds with animals shedding verotoxigenic E. coli O157:H7.

Infection prevention and control interventions in the first outbreak of methicillin-resistant Staphylococcus aureus infections in an equine hospital in Sweden.

BACKGROUND: The first outbreak of methicillin-resistant Staphylococcus aureus (MRSA) infection in horses in Sweden occurred in 2008 at the University Animal Hospital and highlighted the need for improved infection prevention and control. The present study describes interventions and infection prevention control in an equine hospital setting July 2008 - April 2010. METHOD: This descriptive study of interventions is based on examination of policy documents, medical records, notes from meetings and cost estimates. MRSA cases were identified through clinical sampling and telephone enquiries about horses post-surgery. Prospective sampling in the hospital environment with culture for MRSA and genotyping of isolates by spa-typing and pulsed-field gel electrophoresis (PFGE) were performed. RESULTS: Interventions focused on interruption of indirect contact spread of MRSA between horses via staff and equipment and included: Temporary suspension of elective surgery; and identification and isolation of MRSA-infected horses; collaboration was initiated between authorities in animal and human public health, human medicine infection control and the veterinary hospital; extensive cleaning and disinfection was performed; basic hygiene and cleaning policies, staff training, equipment modification and interior renovation were implemented over seven months.Ten (11%) of 92 surfaces sampled between July 2008 and April 2010 tested positive for MRSA spa-type 011, seven of which were from the first of nine sampling occasions. PFGE typing showed the isolates to be the outbreak strain (9 of 10) or a closely related strain. Two new cases of MRSA infection occurred 14 and 19 months later, but had no proven connections to the outbreak cases. CONCLUSIONS: Collaboration between relevant authorities and the veterinary hospital and formation of an infection control committee with an executive working group were required to move the intervention process forward. Support from hospital management and the dedication of staff were essential for the development and implementation of new, improved routines. Demonstration of the outbreak strain in the environment was useful for interventions such as improvement of cleaning routines and interior design, and increased compliance with basic hygienic precautions. The interventions led to a reduction in MRSA-positive samples and the outbreak was considered curbed as no new cases occurred for over a year.

Spatio-temporal evaluation of cattle trade in Sweden: description of a grid network visualization technique.

Understanding the intensity and spatial patterns of animal transfers is of prime importance as geographical moves play an important part in the spread and potential control of contagious animal diseases of veterinary importance. For the purpose of visualizing all registered between-herd animal movements in Sweden between 1 July 2005 and 31 December 2008 by map animation, a grid network technique based on the Bresenham line algorithm was developed. Potential spatio-temporal clustering of animals registered as sold or purchased based on location and month of trade was also detected and tested using a spatial scan statistic. Calculations were based on data from 31,375 holdings and 3,487,426 head of cattle. In total, 988,167 between-herd movements of individual bovines were displayed in a sequence of maps covering three and a half years by 2-week intervals. The maps showed that several cattle movements, both short- and long-distance, take place in Sweden each week of the year. However, most animals (75%) were only registered at one single holding during the study period and 23% were sold to a different holding once. Spatial scan statistics based on data from the year 2008 indicated uneven distributions of purchased or sold animals in space and time. During each autumn, there was an increase in cattle movements and October and November showed significantly more cases of sold or purchased animals (relative risk ~1.7, p = 0.001). Based on the results, we conclude that cattle trade is constantly active at a considerable level. This, in combination with possibly insufficient biosecurity routines applied on many farms, constitutes a risk that contagious diseases are spread in the population. The grid network maps were generated through the use of open-source tools and software in order to decrease software costs and facilitate sharing of programme code. In addition, the technique was based on scripts that allow for the inclusion of iterative processes and that comprise all main parts of map creation. Thereby, a large number of maps can be generated and the demands for high reproducibility are met.