Contagious mastitis pathogens can be transmitted through milking. However, previously published simulation models, such as MiCull, have not directly taken this into account. We have reimplemented the MiCull model to model transmission of contagious mastitis pathogens through milking in a milking parlor. This additional complexity requires a substantial increase in computations and a need to structure the program code to make it more flexible for future use. The aim of this paper was threefold: First, to implement the new model in a faster programming language; secondly, to describe the new model, in particular transmission of a contagious mastitis pathogen through milking; and thirdly, to compare three different milking order strategies in regards to prevalence and incidence of intramammary infections. For each scenario, 500 herds with 200 cows each were simulated over 10 years. The model was calibrated using available mastitis parameters from the literature. We hypothesized that milking order should have a considerable effect on disease transmission, especially if the infected cows with clinical enter the milking parlor first and thereby have a high risk of infecting the following cows. The milking order scenarios examined were random milking order and milking clinical cases first, or last. Unexpectedly, there were no large differences between these scenarios for reasonably sized infection rates corresponding to a herd with a moderate level of clinical mastitis in the herd. Larger differences are expected to be found in herds with very high infection rates. We have developed a transmission simulation model of mastitis pathogens using a new mode of transmission by milking order. We expect that this new version of MiCull will be useful for both researchers and advisors since it is flexible, can be fitted to various in-herd situations and the computations are fast.

Lactational treatment of bovine mastitis is a major contributor to antibiotic consumption in dairy cattle and is, therefore, important to address in light of the increasing problem of antibiotic resistance. In this large-scale database-based retrospective observational study, we combined electronic health records and routinely measured somatic cell counts from individual cows to create an overview of lactational mastitis treatment in Danish dairy herds from 2010 to 2019. Furthermore, posttreatment somatic cell count was used to approximate treatment success in terms of cytological cure. A generalized logistic regression with mixed effects was performed to combine knowledge on cow-level factors (treatment-, pathogen-, and cow-related) with the new infection risk at the herd level, and to explore the relative effect on cytological cure. The investigation revealed that the total number of lactational treatments appears to have decreased steadily over the study period, whereas treatment duration increased slightly. The proportion of cases treated with penicillin-based protocols and the proportion of milk samples sent for pathogen analysis also decreased. Meanwhile, results from the statistical analysis confirm the importance of cow-related factors, such as parity and lactation stage, for the probability of cytological cure following lactational treatment of mastitis. However, they also disclose that factors that are easier to adjust, such as optimizing treatment duration, including knowledge on causative pathogens and improving the herd-level new infection risk that can be used to positively influence the outcome. Application of this knowledge could potentially assist in promoting a more prudent use of antibiotics for dairy cattle in the future.

Position data from real-time indoor positioning systems are increasingly used for studying individual cow behavior and social behavior in dairy herds. However, missing data challenges achieving reliable continuous activity monitoring and behavior studies. This study investigates the pattern of missing data and alternative interpolation methods in ultra-wideband based real-time indoor positioning systems in a free-stall barn. We collected 3 months of position data from a Swedish farm with around 200 cows. Data sampled for 6 days from 69 cows were used in subsequent analyzes to determine the location and duration of missing data. Data from 20 cows with the most reliable tags were selected to compare the effects of four different interpolation methods (previous, linear interpolation, cubic spline data interpolation and modified Akima interpolation). By comparing the observed data with the interpolations of the simulated missing data, the mean error distance varied from around 55 cm, using the previously last observed position, to around 17 cm for modified Akima. Modified Akima interpolation has the lowest error distance for all investigated activities (rest, walking, standing, feeding). Larger error distances were found in areas where the cows walk and turn, such as the corner between feeding and cubicles. Modified Akima interpolation is expected to be useful in the subsequent analyses of data gathered using real-time indoor positioning systems.

Modern dairy cattle farms are usually equipped with cubicle systems to provide cows with comfortable condi-tions for lying down and resting. Cows are free to choose any cubicle they want, but in reality, they do not distribute themselves uniformly throughout the barn. There are many factors that affect where a cow lies down, such as hierarchy of a cow, access to resources, cow traffic nearby, etc. In this study, we used real-time location system data from two commercial farms to examine patterns of cubicle occupancy in relation to parity and lactation stage. We summarized cubicle occupancy over several days and compared different areas of the barn. Our findings suggest that, in general, there was a higher occupancy of cubicles close to the feeding areas. High parity cows lay down more frequently in cubicles close to the milking area as opposed to first lactation cows that tend to occupy less busy areas of the barn. The overall conclusion is that cubicle occupancy is not uniform throughout the barn, and patterns related to parity and DIM are seen. This information can be important for future studies on spread of diseases and for management purposes.

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.

Staphylococcus aureus (S. aureus) strains with considerable genetic and phenotypic differences have previously been identified. The economic and epidemiologic impact of S. aureus mastitis has been investigated, but none of these studies took differences between strains into account. Here we aimed to investigate how differences between S. aureus strains affect the economic and epidemiologic outcome of various intervention strategies against clinical and subclinical intramammary infections. Five S. aureus strains were modelled using a stochastic bio-economic model simulating a dairy herd of 200 cows using single-day time steps. The strain characteristics of the five simulated S. aureus strains (general, contagious, spill-over, clinical and persistent) were based on divergent phenotypes as described in literature. Outcomes of the model included incidence (both clinical and subclinical), number of antibiotic treatment days, number of culled cows, and net income. Intervention strategies against clinical and subclinical intramammary infections were based on (variations of) intramammary antibiotic treatment, testing, and culling. Both single and multiple pathogen (intramammary infection caused by S. aureus, Escherichia coli, and non-aureus staphylococci) scenarios were simulated to determine the effect of the five S. aureus strains on the impact of 19 different intervention strategies. The results showed that the incidence (both clinical and subclinical), number of treatment days, number of culled cows, and net income varied considerably for the different S. aureus strains. Comparison of the model outcomes within and between strains showed that for most intervention strategies the relative impact differed per strain. However, the intervention strategy with the best outcome for most variables and strains was the culling of cows with a recovery probability lower than 50%. This shows that the relative economic and epidemiologic impact of most of the modelled intervention strategies were strain-dependent, while some intervention strategies were not strain-dependent. From this, we conclude that, depending on the intervention strategy applied on a farm, it could be advantageous to type S. aureus to determine whether it would be economically and epidemiologically beneficial for the existing intervention strategy to be changed.

Culling is an important management tool in dairy herds, as it affects herd economics and animal welfare. In relation to health, culling is usually studied as a consequence of health disorders, but it can also be regarded as a tool to manage health in the herd by making strategic culling decisions. In this study, we used data from the Danish Cattle Database in herd-wise survival analyses to investigate factors associated with culling, in relation to udder health, in 1,452 dairy herds. The data included milk yield, somatic cell counts (SCC), parity, and different disease related factors with a special focus on udder health. In each herd, observations and survival analyses were divided into five groups: mid lactation heifers, late lactation heifers, early lactation cows, mid lactation cows, and late lactation cows. The results showed that a high average milk yield reduced the culling hazard, and a number of risk factors (e.g., parity, a high SCC or treatment of mastitis) were associated with an increased hazard for culling. Importantly, the strength and direction of many of these associations was dependent on the lactation stage. The resulting coefficients were further analysed by principal component analysis and clustering to explore variations in culling risk factors amongst herds. In some herds, parity was an important factor for culling, while in other herds, average milk yield, SCC, or udder health were more important factors. However, clusters were substantially overlapping, indicating that the decision making process underlying culling is complex and multifactorial.

The objective of this study was to evaluate and compare different combinations of intervention strategies for contagious or opportunistic subclinical and clinical intramammary infections (IMI). We simulated two different Danish dairy cattle herds with ten different intervention strategies focusing on cow-specific treatment or culling, including three baseline strategies without subclinical interventions. In one herd, the main causative pathogen of IMI was Staphylococcus (S.) aureus. In the other herd, Streptococcus (St.) agalactiae was the main causative agent. For both herds, we investigated costs and effectiveness of all ten intervention strategies. Intervention strategies consisted of measures against clinical and subclinical IMI, with baselines given by purely clinical intervention strategies. Our results showed that strategies including subclinical interventions were more cost-effective than the respective baseline strategies. Increase in income and reduction of IMI cases came at the cost of increased antibiotic usage and an increased culling rate in relation to IMI. However, there were differences between the herds. In the St. agalactiae herd, the clinical intervention strategy did not seem to have a big impact on income and number of cases. However, intervention strategies which included cow-specific clinical interventions led to a higher income and lower number of cases in the S. aureus herd. The results show that intervention strategies including interventions against contagious or opportunistic clinical and subclinical IMI can be highly cost-effective, but should be herd-specific.

Intramammary infections (IMI) in dairy cattle lead to economic losses for farmers, both through reduced milk production and disease control measures. We present the first strain-, cow- and herd-specific bio-economic simulation model of intramammary infections in a dairy cattle herd. The model can be used to investigate the cost-effectiveness of different prevention and control strategies against IMI. The objective of this study was to describe a transmission framework, which simulates spread of IMI causing pathogens through different transmission modes. These include the traditional contagious and environmental spread and a new opportunistic transmission mode. In addition, the within-herd transmission dynamics of IMI causing pathogens were studied. Sensitivity analysis was conducted to investigate the influence of input parameters on model predictions. The results show that the model is able to represent various within-herd levels of IMI prevalence, depending on the simulated pathogens and their parameter settings. The parameters can be adjusted to include different combinations of IMI causing pathogens at different prevalence levels, representing herd-specific situations. The model is most sensitive to varying the transmission rate parameters and the strain-specific recovery rates from IMI. It can be used for investigating both short term operational and long term strategic decisions for the prevention and control of IMI in dairy cattle herds.

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.

Societal pressure to limit the use of antibiotics in livestock production systems, including dairy cattle systems, is consistently increasing. To motivate farmers to reduce antibiotic usage, it is important to understand the factors that determine whether a cow will be treated with antibiotics or not. If farmers' usual practices regarding antibiotic treatments are taken into account, they may be motivated to adopt control measures that can facilitate prudent use of antibiotics and are at the same time cost-effective. In this study, we analyzed database recordings of milk yield and somatic cell count from the routine milk recording scheme, clinical registrations of mastitis and PCR results, and cow factors such as days in milk and parity in relation to antibiotic treatments for 518 dairy herds in Denmark. Farm-wise logistic regressions were used to predict antimicrobial treatment based on these factors. The resulting regression coefficients of 422 herds were further analyzed by principal component analysis and clustering to determine the driving predictors for treatment in different groups of farms. The results showed that determinants that were most important for predicting antibiotic treatments vary from one farm to another. Health indicators such as PCR or somatic cell count were most indicative for treatment on some farms, whereas other groups seemed to depend more on production factors (milk yield) or later culling of the cows. This shows that farmers behave differently and differences can be identified in register data. This information can be considered when developing cost-effective herd-specific control measures of mastitis to promote prudent use of antibiotics in Danish dairy cattle farms.

We will further develop an existing bioeconomic model to model the spread of pathogens causing mastitis within a dairy cattle herd. The model will then be used for a subsequent assessment of the impact of strategies on herd-and cow-specific level to monitor and control mastitis.