Estimating potential epidemic size following introduction of a long-incubation disease in scale-free connected networks of milking-cow movements in Ontario, Canada

We used the movements of adult milking cows among farms enrolled in the Dairy Herd Improvement (DHI) program in Ontario to explore the size of an epidemic that might result from farm-to-farm movements of cows in the Province if a reportable long-incubation infection like tuberculosis (TB) were introduced and not detected for 1-3 years after introduction. A directed network was created for each year (2004-2006) using all pairs of individual shipments, defined as the movement of one or more cows on a single day, from a single source DHI farm to a single recipient DHI farm. A 3-year network was also developed that included all cow shipments that took place during these 3 years. The lower and upper bounds of potential maximal epidemic size were estimated using four network-analysis measures: (1) the farm out-degree, (2) the size of the largest strong and weak components, (3) the bow-tie approach, classifying farms into six different areas of a directed network and (4) the infection chain of a farm. All four of the DHI movement networks were found to be small-world, indicating that infection could spread over considerable distances by shipments that linked potentially distant clusters of farms. The networks were also scale-free, indicating most farms had relatively few connections to other farms, while there were a few highly connected farms. Characterization of the yearly networks showed that 41-47% of DHI farms were not involved in any cow shipments and were therefore not at risk of infection from this movement network; furthermore, if infection were introduced into a DHI farm that shipped animals that year, the infection would have stopped at that farm (or at least, not been passed on by shipment of adult milking cows) >50% of the time, and 75% of the time only one more DHI farm would have become infected through animal movements. Compared to the infection chain, which accounted for both the direction and the time sequence of shipments in the movement network, the other network-analysis measures provided biased estimates of potential epidemic size. The bow-tie approach provided a schematic representation of the level of risk of each farm in the network in spreading an infection, but overestimated the lower- and upper-bound measures of potential epidemic size because it did not account for the time sequence of shipments. Our infection-chain results suggest that introducing a long-incubation disease into the network of farms enrolled in the DHI program in Ontario that was not identified until 12 months after the incursion would, in a worst-case scenario, have resulted in 168 farms (representing 5% of all Ontario DHI herds) being infected as a consequence of adult cow movements among DHI farms. This estimate increased to 850 farms (26% of all DHI herds) if the infection were not identified for 36 months.     

Predictive spatial modelling of alternative control strategies for the foot-and-mouth disease epidemic in Great Britain, 2001

A spatial simulation model of foot-and-mouth disease was used in March and early April 2001 to evaluate alternative control policies for the 2001 epidemic in Great Britain. Control policies were those in operation from March 20, 2001, and comprised a ban on all animal movements from February 23, 2001, and a stamping-out policy. Each simulation commenced with the known population of infected farms on April 10, 2001, and ran for 200 days. For the control policy which best approximated that actually implemented from late March, the model predicted an epidemic of approximately 1800 to 1900 affected farms, and estimated that the epidemic would be eradicated between July and October 2001, with a low probability of continuing beyond October 2001. This policy included the slaughter-out of infected farms within 24 hours, slaughter of about 1.3 of the surrounding farms per infected farm within a further 48 hours, and minimal interfarm movements of susceptible animals. Delays in the slaughter of animals on infected farms beyond 24 hours after diagnosis slightly increased the epidemic size, and failure to achieve pre-emptive slaughter on an adequate number of at-risk farms substantially increased the expected size of the epidemic. Vaccination of up to three of the most outbreak-dense areas carried out in conjunction with the adopted control policy reduced the predicted size of the epidemic by less than 100 farms. Vaccination of buffer zones (designed to apply available vaccine and manpower as effectively as possible) carried out in place of the adopted control policy allowed the disease to spread out of control, producing an epidemic involving over 6000 farms by October 2001, with no prospect of immediate eradication.     

Clinical and laboratory investigations of five outbreaks of foot-and-mouth disease during the 2001 epidemic in the United Kingdom

Clinical and laboratory investigations of five outbreaks of foot-and-mouth disease (FMD) were made during the early stages of the 2001 epidemic in the UK. The first outbreak, confirmed on February 20, was at an abattoir in Essex which specialised in the processing of culled sows and boars. On February 23, the disease was confirmed at a pig farm in Northumberland which held cull sows and boars fed on waste food; the findings indicated that it was the first of the five premises to be infected. The disease had probably been present since early February, and it was the most likely origin of the epidemic. The other premises investigated were a waste food-fed cull sow/boar pig unit in Essex, approximately 30 km from the abattoir, which was probably infected at the same time or before the abattoir, a sheep and cattle farm approximately 6 km from the Northumberland pig farm, which was probably infected by airborne virus from it in the period immediately before February 13, and a sheep and cattle farm in Devon which had clinical disease from February 20 and was probably infected by sheep transported from Northumberland on February 13 which arrived on February 15.     

Consideration of different outbreak conditions in the evaluation of preventive culling and emergency vaccination to control foot and mouth disease epidemics

In recent foot and mouth disease outbreaks, many healthy animals have been culled to prevent disease transmission. Emergency vaccination is discussed as an alternative to culling of unaffected animals. A spatial and temporal Monte–Carlo simulation model was used to compare preventive culling and emergency vaccination. Different outbreaks are described using additional influence factors such as airborne spread, farm density, type of index-case farm and delay until establishment of the control strategies. The fewest farms were infected establishing a combined strategy including a 1 km preventive culling and 1–10 km emergency vaccination zone around each outbreak farm. Taking the number of culled and vaccinated farms into account, vaccination around the first diagnosed farm combined with the baseline strategy (culling of outbreak farms, protection and surveillance zone, contact tracing) is to be preferred. In the present study, emergency vaccination was an effective control strategy especially in densely populated regions.     

Identification of geographic factors associated with early spread of foot-and-mouth disease

OBJECTIVE: To explore whether early analysis of spatial data may result in identification of variables associated with epidemic spread of foot and mouth disease. SAMPLE POPULATION: 37 farms with infected cattle (ie, case farms) reported within the first 6 days of the 2001 Uruguayan foot-and-mouth disease epidemic. PROCEDURE: A georeferenced database was created and retrospective analysis was performed on case farm location in relation to farm density, cattle density, farm type (ie, beef vs dairy cattle production), road density, case farm distance to the nearest road, farm size, farm ownership, and day of infection. Mean or median results of 1 to 3 day versus 4 to 6 day spatial data were compared. Spatial-temporal associations were investigated by correlation analysis. RESULTS: Comparison of mean or median values between the first 3 days and days 4 to 6 of the epidemic and results of correlation analysis indicated a significant increase in road density, cattle density, and dairy cattle production and a significant decrease in farm size and case farm distance to the nearest road that developed over time. A route that linked most case farms by the shortest possible distance and also considered significantly associated variables was created. It included 86.1% of all case farms reported by 60 days into the epidemic. CONCLUSIONS AND CLINICAL RELEVANCE: Epidemic direction can be assessed on the basis of road density and other spatial variables as early as 6 days into an epidemic. Epidemic control areas may be more effectively identified if local and regional georeferenced data are considered.     

Risk factors for the introduction of high pathogenicity Avian Influenza virus into poultry farms during the epidemic in the Netherlands in 2003

An epidemic of high pathogenicity Avian Influenza (HPAI) occurred in the Netherlands in 2003. A census survey of 173 infected and 401 uninfected commercial poultry farms was carried out to identify factors associated with the introduction of the HPAI virus into poultry farms. Data on farm size, production characteristics, type of housing, presence of cattle and pigs were gathered by the National Inspection Service for Livestock and Meat from all farms included in this study. For each risk factor (RF) available for analysis, the Mantel-Haenszel odds ratio was calculated (stratified on farm size and housing type). We found an increased risk of HPAI virus introduction in layer finisher type poultry: OR = 2.05 (95% confidence interval, CI = 1.29-3.27). An explanation for this increased risk is the high number of contacts between these farms, especially via cardboard egg trays used for removal of eggs during the epidemic. Our analysis did not indicate significant differences between the infected and uninfected farms with regard to housing type, presence of cattle or pigs. Since layer finisher type farms are assumed to be at higher risk for HPAI virus introduction, more specific control measures might be applied in future outbreaks.     

Monte Carlo simulation of classical swine fever epidemics and control. I. General concepts and description of the model

A Monte Carlo simulation has been developed to describe the spread of classical swine fever virus between farms within a certain region. The data of the farms can be imported and considered individually. Transmission occurs via the infection routes direct animal and indirect person and vehicle contact, as well as by contaminated sperm and local spread. Parameters, such as incubation period and probability of detection, can be varied by the user and their impact on disease spread can be studied. The control measures stamping-out, movement control and pre-emptive slaughter in circular restriction areas as well as contact tracing can be applied and their effect on disease spread can thus be analysed. The numbers of culled and restricted farms and animals per epidemic and per day within an epidemic, the epidemic duration and the total length of restrictions per restricted farm are given. In an example, simulation runs were performed under the condition of application of all four-control measures. Because no real farm data were available, a test area was generated stochastically with a farm density of 1.3 farms/km(2). The distributions of the number of infected farms per epidemic and the epidemic length are shown.     

A cross-sectional study to show Eperythrozoon ovis infection is prevalent in Western Australian sheep farms

A serological survey and risk factor study was conducted to estimate the prevalence of Eperythrozoon ovis infection in Western Australian weaner sheep, the prevalence of farms with infected sheep, and to identify factors affecting initiation and maintenance of infection on the farm. The study was conducted on 91 farms, purposively chosen from 41 randomly selected regional shires stratified by sheep number and rainfall zones. Twenty sheep were selected systematically from a mixed-sex flock on each farm and tested for serum antibody to E ovis using an enzyme-linked immunosorbent assay. Information on putative risk factors was collected using an interview questionnaire. Antibody to E ovis was detected in 4.5% of sheep on 47% of the farms sampled. The prevalence of E ovis infection in sheep was estimated at the 95% confidence level to be between 3.6 and 5.5%, and the prevalence of farms with infected sheep was estimated to be between 37.5 and 56.5%. Most farms with serological evidence of infection occurred in the Great Southern agricultural region (79.5%), south-east of Perth through to Albany (latitude 32 to 34 degrees S, longitude 116 to 120 degrees E), and in the Northern region (12.8%) surrounding Geraldton (latitude 29 degrees S, longitude 114 degrees E). There were significantly more farms (P less than 0.05) with evidence of infection in the Great Southern region compared to the Central region between Geraldton and Perth, and on farms in the region south compared to north of latitude 32 degrees S. None of the putative risk factors examined in the questionnaire were associated with serological evidence of infection on the farm.(ABSTRACT TRUNCATED AT 250 WORDS)     

Some Factors Affecting Variation in Milk Yield in Crossbred Dairy Cows on Smallholder Farms in North-East Tanzania

A 2-year longitudinal survey was carried out to investigate factors affecting milk yield in crossbred cows on smallholder farms in and around an urban centre. Sixty farms were visited at approximately 2-week intervals and details of milk yield, body condition score (BCS) and heart girth measurements were collected. Fifteen farms were within the town (U), 23 farms were approximately 5 km from town (SU), and 22 farms approximately 10 km from town (PU). Sources of variation in milk yield were investigated using a general linear model by a stepwise forward selection and backward elimination approach to judge important independent variables. Factors considered for the first step of formulation of the model included location (PU, SU and U), calving season, BCS at calving, at 3 months postpartum and at 6 months postpartum, calving year, herd size category, source of labour (hired and family labour), calf rearing method (bucket and partial suckling) and parity number of the cow. Daily milk yield (including milk sucked by calves) was determined by calving year (p < 0.0001), calf rearing method (p = 0.044) and BCS at calving (p < 0.0001). Only BCS at calving contributed to variation in volume of milk sucked by the calf, lactation length and lactation milk yield. BCS at 3 months after calving was improved on farms where labour was hired (p = 0.041) and BCS change from calving to 6 months was more than twice as likely to be negative on U than SU and PU farms. It was concluded that milk production was predominantly associated with BCS at calving, lactation milk yield increasing quadratically from score 1 to 3. BCS at calving may provide a simple, single indicator of the nutritional status of a cow population.     

Epidemiology of the epidemic of bovine anaemia associated with Theileria orientalis (Ikeda) between August 2012 and March 2014

AIMS: To describe the epidemiology of the epidemic of bovine anaemia associated with Theileria orientalis infection (TABA) in New Zealand between 30 August 2012 and 4 March 2014.

METHODS: Blood samples and associated data were obtained from cases of TABA. The case definition for TABA was met when piroplasms were present on blood smears and the haematocrit was ≤0.24?L/L. Samples were analysed using quantitative PCR (qPCR) assays for the detection of T. orientalis Ikeda type. Only cases that were positive in the qPCR assays were included in the analysis. A case herd was defined as a herd that had ≥1 animal positive for T. orientalis Ikeda.

Movement records for farms were accessed through the national animal identification and tracing scheme. The OR for cattle movements onto a case farm compared to a non-case farm was estimated using a generalised estimating equation model and the geodesic distance for movements onto case and non-case farms compared using Student's t-test. The kernel-smoothed risk of disease at the farm level was calculated using an extraction map and the clustering of diseased farms in time and space was measured using the spatial temporal inhomogeneous pair correlation function.

RESULTS: In the first 18 months there were 496 case herds; 392 (79%) were dairy and 104 (21%) beef herds. Of 882 individual cases, 820 (93.0%) were positive for T. orientalis Ikeda in the qPCR assays. Case herds were initially clustered in the Northland, then the Waikato regions. The OR for a case farm compared to a non-case farm having ≥1 inward cattle movements was 2.03 (95% CI=1.52–2.71) and the distance moved was 26 (95% CI=20.8–31.3) km greater for case farms. The risk of disease was highest in a north, north-eastern to south, south-western belt across the Waikato region. The spatial-temporal analysis showed significant clustering of infected herds within 20–30 days and up to 15?km distant from a case farm.

CONCLUSIONS: Theileria orientalis Ikeda type is likely to have been introduced into regions populated with naïve cattle by the movement of parasitaemic cattle from affected areas. Local spread through dispersed ticks then probably became more important for disease transmission between herds once the disease established in a new area.

CLINICAL RELEVANCE: Dairy and beef farming in the North Island of New Zealand will be significantly changed in the coming years by the incursion of this new disease.     

Models for regional heartwater epidemiology in a variable environment

A model of the epidemic dynamics of heartwater within a cattle production unit was presented by Yonow et al. (1998). Here, the model is expanded to a region consisting of several farms to study the effect of environmental variability on control strategies. We have shown that: * In a region, where the environment of each farm is modelled with constant epidemiologic parameter values, while the between-farm parameter values differ, regional variation in the removal rate of infected cattle increases the average fraction of infected cattle across the region, while regional variation in the transmission rate of infection from ticks to cattle decreases the average fraction of infected cattle, thereby requiring control measures that keep the removal rate uniform and the transmission rate variable. * In a region, where in addition to regional variation between farms, the epidemiologic parameters of each farm are time-variant, then temporal variation in both the transmission rate and removal rate increases the average fraction of infected cattle across the region, thereby requiring control measures that keep both parameters uniform.     

Transmission parameters of highly pathogenic avian influenza (H7N1) among industrial poultry farms in northern Italy in 1999-2000

We estimated between-farm transmission parameters of the highly pathogenic avian-influenza (HPAI) epidemic that struck the poultry industry of northern Italy (including turkeys, layer hens, broilers, gamebirds, and waterfowl) from December 1999 through April 2000. We estimated the average number of susceptible farms that were infected with HPAI virus by each infectious farm during a day (β) with a generalised linear model (GLM). The HPAI's reproductive ratios (R_h; the average number of new infected farms (IFs) that were caused by an infectious farm) were calculated separately for the regions of Lombardy and Veneto, where 382 out of 413 (92.5%) of IFs were located. In both regions, R_h decreased to 1 during the second month of the epidemic (showing that its containment had been initiated). Subsequently, during the last two months of the epidemic, β and R_h were reduced to 0.04/day and 0.6, respectively, in Veneto and to 0.07/day and 0.8 in Lombardy. The reduction of the susceptible population through strict control measures, including pre-emptive slaughter of at-risk poultry flocks, was implemented to a greatest extent in Veneto and this might have been associated with a more rapid control of the epidemic in this region than in Lombardy.     

Use of high spatial resolution satellite imagery to characterize landscapes at risk for bluetongue

The recent and rapid spread in the Mediterranean Basin of bluetongue, a viral disease of ruminants transmitted by some species of Culicoides (biting midges), highlights the necessity of determining the conditions of its emergence. This study uses high spatial resolution satellite imagery and methods from landscape ecology science to identify environmental parameters related to bluetongue occurrence in Corsica, a French Mediterranean island where the disease occurred for the first time in 2000. A set of environmental variables recorded in the neighborhood of 80 sheep farms were related to case occurrence through a logistic regression model computed within three subsequent buffer distances of 0.5, 1 and 2 km. The results reveal the role of landscape metrics, particularly those characterizing land-use units such as prairies and woodlands, as well as farm type, latitude and sunshine to explain the presence of bluetongue. Internal and external validation both indicate that the best results are obtained with the 1 km buffer size model (area under Receiver Operating Characteristic curve = 0.9 for internal validation and 0.81 for external validation). The results show that high spatial resolution remote sensing (i.e. 10 m pixels) and landscape ecology approaches contribute to improving the understanding of bluetongue epidemiology.     

Risk factors for hazard of release from salmonella-control restriction on Swedish cattle farms from 1993 to 2002

In Sweden, only a few cattle farms are infected with salmonella each year and this can be attributed to the Swedish salmonella control programme. All findings of salmonella in animals, feed and food of animal origin are notifiable and restrictions are always put on infected herds until they have been cleaned up from the infection. However, there has been concern about increasing costs for clean-up of salmonella-infected farms as well as increasing length of the restriction periods. Our aim was to investigate potential risk factors associated with the length of restriction periods on Swedish cattle farms between 1993 and late 2002. All 112 cattle farms that were notified to the Swedish Board of Agriculture as infected with salmonella during the study period, were included in this longitudinal and retrospective study. The putative risk factors were analysed using the proportional-hazards model. There was a lower hazard for release from salmonella control restrictions after the European Union (EU) accession in 1995, and/or change of testing from one to two negative herd tests for release of restrictions (hazard ratio (HR)=0.56, 95% confidence interval (CI)=0.38, 0.84), for every additional number of 100 cows (HR=0.83; CI=0.7, 0.97), if rodents and/or wild birds were abundant (HR=0.5, CI=0.27, 0.98) and if there was more than one farm site in the company (HR=0.47, CI=0.28, 0.81).     

Prediction of penicillin resistance in Staphylococcus aureus isolates from dairy cows with mastitis, based on prior test results

AIM: To gauge how well prior laboratory test results predict in vitro penicillin resistance of Staphylococcus aureus isolates from dairy cows with mastitis. METHODS: Population-based data on the farm of origin (n=79), genotype based on pulsed-field gel electrophoresis (PFGE) results, and the penicillin-resistance status of Staph. aureus isolates (n=115) from milk samples collected from dairy cows with mastitis submitted to two diagnostic laboratories over a 6-month period were used. Data were mined stochastically using the all-possible-pairs method, binomial modelling and bootstrap simulation, to test whether prior test results enhance the accuracy of prediction of penicillin resistance on farms. RESULTS: Of all Staph. aureus isolates tested, 38% were penicillin resistant. A significant aggregation of penicillin-resistance status was evident within farms. The probability of random pairs of isolates from the same farm having the same penicillin-resistance status was 76%, compared with 53% for random pairings of samples across all farms. Thus, the resistance status of randomly selected isolates was 1.43 times more likely to correctly predict the status of other isolates from the same farm than the random population pairwise concordance probability (p=0.011). This effect was likely due to the clonal relationship of isolates within farms, as the predictive fraction attributable to prior test results was close to nil when the effect of within-farm clonal infections was withdrawn from the model. CONCLUSIONS: Knowledge of the penicillin-resistance status of a prior Staph. aureus isolate significantly enhanced the predictive capability of other isolates from the same farm. In the time and space frame of this study, clinicians using previous information from a farm would have more accurately predicted the penicillin-resistance status of an isolate than they would by chance alone on farms infected with clonal Staph. aureus isolates, but not on farms infected with highly genetically heterogeneous bacterial strains.     

The structure, dynamics and movement patterns of the Australian sheep industry

Objective To describe the structure of Australia's sheep industries and the movement of sheep to enable examination of the potential for animal movements to spread disease between farms. Procedure The structure, size, marketing and movement patterns of Australian sheep farms was determined through (i) review of data published by the Australian Bureau of Statistics and Australian Bureau of Agricultural and Resource Economics, (ii) interviews with producers and saleyard managers and (iii) expert opinion. Results Twelve geographic regions are described, based on the type and extent of sheep farming in each region. Five production sectors were identified within the Australian sheep industry, with the proportion of each varying between the geographic regions. Over the past 20 years, the industry has decreased in size and contracted from the northern and central areas of Australia. Movement of sheep onto the majority (79%) of properties was limited to the introduction of less than 50 stud rams annually, although cross‐bred‐ and wether‐based farms introduced up to 2000 sheep annually; 75% of sheep movements occurred over distances less than 200 km, but stud rams moved up to 500 km. An increasing percentage of movements off farms was direct to abattoirs and over 80% of sheep sold through saleyards were purchased by abattoirs. Conclusions The majority of Australian sheep farms operate as self‐replacing enterprises and introduce few stock. In addition, most sheep movements occur over distances of less than 200 km and therefore sheep movements within Australia have only a limited potential to spread disease over larger distances.     

Some Factors Affecting Reproductive Success in Crossbred Dairy Cows on Smallholder Farms in Coastal North-East Tanzania

A 2-year longitudinal survey was carried out to investigate factors affecting reproduction in crossbred cows on smallholder farms in and around an urban centre. Sixty farms were visited at approximately 2-week intervals and details of reproductive traits and body condition score (BCS) were collected. Fifteen farms were within the town (U), 23 farms were approximately 5 km from town (SU), and 22 farms approximately 10 km from town (PU). Sources of variation in reproductive traits were investigated using a general linear model (GLM) by a stepwise forward selection and backward elimination approach to judge important independent variables. Factors considered for the first step of formulation of the model included location (PU, SU and U), type of insemination, calving season, BCS at calving, at 3 months postpartum and at 6 months postpartum, calving year, herd size category, source of labour (hired and family labour), calf rearing method (bucket and partial suckling) and parity number of the cow. The effects of the independent variables identified were then investigated using a non-parametric survival technique. The number of days to first oestrus was increased on the U site (p = 0.045) and when family labour was used (p = 0.02). The non-parametric test confirmed the effect of site (p = 0.059), but effect of labour was not significant. The number of days from calving to conception was reduced by hiring labour (p = 0.003) and using natural service (p = 0.028). The non-parametric test confirmed the effects of type of insemination (p = 0.0001) while also identifying extended calving intervals on U and SU sites (p = 0.014). Labour source was again non-significant. Calving interval was prolonged on U and SU sites (p = 0.021), by the use of AI (p = 0.031) and by the use of family labour (p = 0.001). The non-parametric test confirmed the effect of site (p = 0.008) and insemination type (p > 0.0001) but not of labour source. It was concluded that under favourable conditions (PU site, hired labour and natural service) calving intervals of around 440 days could be achieved.     

Developing stochastic epidemiological models to quantify the dynamics of infectious diseases in domestic livestock

A stochastic model describing disease transmission dynamics for a microparasitic infection in a structured domestic animal population is developed and applied to hypothetical epidemics on a pig farm. Rational decision making regarding appropriate control strategies for infectious diseases in domestic livestock requires an understanding of the disease dynamics and risk profiles for different groups of animals. This is best achieved by means of stochastic epidemic models. Methodologies are presented for 1) estimating the probability of an epidemic, given the presence of an infected animal, whether this epidemic is major (requires intervention) or minor (dies out without intervention), and how the location of the infected animal on the farm influences the epidemic probabilities; 2) estimating the basic reproductive ratio, R0 (i.e., the expected number of secondary cases on the introduction of a single infected animal) and the variability of the estimate of this parameter; and 3) estimating the total proportion of animals infected during an epidemic and the total proportion infected at any point in time. The model can be used for assessing impact of altering farm structure on disease dynamics, as well as disease control strategies, including altering farm structure, vaccination, culling, and genetic selection.     

Evaluating control strategies for outbreaks in BHV1-free areas using stochastic and spatial simulation

Several countries within the EU have successfully eradicated bovine herpesvirus type I (BHV1), while others are still making efforts to eradicate the virus. Reintroduction of the virus into BHV1-free areas can lead to major outbreaks - thereby causing severe economic losses. To give decision-makers more insight into the risk and economic consequences of BHV1 reintroduction and into the effectiveness of various control strategies, we developed the simulation model InterIBR. InterIBR is a dynamic model that takes into account risk and uncertainty and the geographic location of individual farms. Simulation of a BHV1-outbreak in the Netherlands starts with introduction of the virus on a predefined farm type, after which both within-farm and between-farm transmission are simulated. Monitoring and control measures are implemented to simulate detection of the infection and subsequent control. Economic consequences included in this study are related to losses due to infection and costs of control. In the simulated basic control strategy, dairy farms are monitored by monthly bulk-milk tests and miscellaneous farms are monitored by half-yearly serological tests. After detection, movement-control measures apply, animal contacts are traced and neighbour farms are put on surveillance. Given current assumptions on transmission dynamics, we conclude that a strategy with either rapid removal or vaccination of infected cattle does not reduce the number of infected farms compared to this basic strategy - but will cost more to control. Farm type with first introduction of BHV1 has a considerable impact on the expected number of secondarily infected farms and total costs. To limit the number of infected farms and total costs due to outbreaks, we suggest intensifying the monitoring program on farms with a high frequency of cattle trade, and monthly bulk-milk testing on dairy farms.