Predictive modelling of deviation from expected milk yield in transition cows on automatic milking systems.

The transition period is a pivotal time in the production cycle of the dairy cow. It is estimated that between 30% and 50% of all cows experience metabolic or infectious disease during this time. One of the most common and economically consequential effects of disease during the transition period is a reduction in early lactation milk production. This has led to the utilisation of deviation from expected milk yield in early lactation as a proxy measure for transition health. However, to date, this analysis has been used exclusively for the retrospective assessment of transition cow health. Statistical models capable of predicting deviations from expected milk yield may allow producers to proactively manage animals predicted to suffer negative deviations in early lactation milk production. The objective of this retrospective cohort study was first, to explore the accuracy with which cow-level production and behaviour data collected on automatic milking systems (AMS) from 1-3 days in milk (DIM) can predict deviation from expected 30-day cumulative milk yield in multiparous cows. And second, to assess the accuracy with which predicted yield deviations can classify cows into groups which may facilitate improved transition management. Production, rumination, and physical activity data from 31 commercial AMS were accessed. A 3-step analytical procedure was then conducted. In Step 1, expected cumulative yield for 1-30 DIM for each individual cow-lactation was calculated using a mixed effect linear model. In Step 2, 30-Day Yield Deviation (YD) was calculated as the difference between observed and expected cumulative yield. Lactations were then assigned to one of three groups based on their YD, RED Group (0% YD). In Step 3, yield, rumination, and physical activity data from days 1-3 in lactation were used to predict YD using machine learning models. Following external validation, YD was predicted across the test data set with a mean absolute error of 9%. Categorisation of animals suffering large negative deviations (RED group) was achieved with a specificity of 99%, sensitivity of 35%, and balanced accuracy of 67%. Our results suggest that milk yield, rumination and physical activity patterns expressed by dairy cows from 1-3 DIM have utility in the prediction of deviation from expected 30-day cumulative yield. However, these predictions currently lack the sensitivity required to classify cows reliably and completely into groups which may facilitate improved transition cow management.

Quantification of the effect of in-utero events on lifetime resilience in dairy cows.

Currently, the dairy industry is facing many challenges that could affect its sustainability, including climate change and public perception of the industry. As a result, interest is increasing in the concept of identifying resilient animals, those with a long productive lifespan, good reproductive performance and milk yield. There is much evidence that events in utero, i.e., the Developmental Origins of Health and Disease (DOHaD), alter life-course health of offspring and we hypothesized that these could alter resilience in calves, where resilience is identified using lifetime data. The aim of this study was to quantify lifetime resilience scores (LRS) using an existing scoring system based on longevity with secondary corrections for age at first calving and calving interval and to quantify the effects of in-utero events on the LRS using 2 data sets. The first was a large data set of cattle in 83 farms in Great Britain born from 2006 to 2015 and the second was a smaller, more granular data set of cattle born between 2003 and 2015 in the Langhill research herd at Scotland's Rural College. Events during dam's pregnancy included health events (lameness, mastitis, use of an antibiotic or anti-inflammatory medication), the impact of heat stress as measured by temperature-humidity index and perturbations in milk yield and quality (somatic cell count, percentage fat, percentage protein and fat:protein ratio). Daughters born to dams that experienced higher temperature-humidity indexes while they were in-utero during the first and third trimesters of pregnancy had lower LRS. Daughter LRS scores were also lower where milk yields or median fat percentages in the first trimester were low, and when milk yields were high in the third trimester. Dam LRS was positively associated with LRS of their offspring, however, as parity of the dam increased, LRS of their calves decreased. Similarly, in the Langhill herd, dams of a higher parity produced calves with lower LRS. Additionally, dams which recorded a high max locomotion score in the third trimester of pregnancy were negatively associated with lower calf LRS in the Langhill herd. Our results suggest that events that occur during pregnancy have lifelong consequences for the calf's lifetime performance. However, experience of higher temperature-humidity indexes, higher dam LRS scores and mothers in higher parities explained a relatively small proportion of variation in offspring LRS, which suggests that other factors play a substantial role in determining calf LRS scores. While 'big data' can contain a considerable amount of noise, similar findings between the 2 data sets indicate it is likely these findings are real.

Sensitivity and specificity of mobility scoring for the detection of foot lesions in pasture-based Irish dairy cows.

Lameness is an important production disease in dairy cows worldwide and has detrimental effects on cows' welfare, production, and reproductive performance, thus affecting the sustainability of dairy farming. Timely and effective detection of lameness allows for effective treatment, minimizing progression of disease, and maximizing the prognosis of recovery. Mobility scoring (MSc) is a 4 point (0-3) visual lameness scoring system that is the industry standard in several countries. However, few studies have examined the sensitivity (Se) and specificity (Sp) of MSc to detect foot lesions. The aim of this observational study was to determine the Se and Sp of MSc to detect foot lesions in dairy cattle in a pasture-based system. Five hundred ninety-five primi- and multiparous cows were randomly selected from 12 commercial Irish dairy farms and recruited for the study. Recruited cows were mobility scored and passed through a foot-paring crate where all 4 feet were lifted for examination. The team recorded the anatomical location and severity of any foot lesions present based on appearance only. Then, based on the type and severity of the lesions present, cows were classified according to 3 case definitions case definition 1: Any lesion present; case definition 2: Moderate lesions present (excluding minor lesions expected to have a low probability of affecting gait); and case definition 3: Severe lesions present (only including lesions most likely to result in a detectable gait abnormality). Sensitivity and Sp of MSc was calculated based on a threshold of MSc ≥2, defined as impaired (MSc = 2) or severely impaired (MSc = 3) mobility for each of the 3 case definitions, at the overall level and disaggregated by parity. The overall cow-level lesion prevalence based on the case definition 1 was 0.54 with significant between-herd variation. The overall Se and Sp of MSc for the detection of foot lesions were 0.18 and 0.96, 0.35 and 0.94, 0.43 and 0.94 for the case definitions 1, 2, and 3, respectively. Our findings showed poor Se, but high Sp of MSc for the detection of cows with foot lesions in a pasture-based system.

The use of machine learning to predict somatic cell count status in dairy cows post-calving.

Udder health remains a priority for the global dairy industry to reduce pain, economic losses, and antibiotic usage. The dry period is a critical time for the prevention of new intra-mammary infections and it provides a point for curing existing intra-mammary infections. Given the wealth of udder health data commonly generated through routine milk recording and the importance of udder health to the productivity and longevity of individual cows, an opportunity exists to extract greater value from cow-level data to undertake risk-based decision-making. The aim of this research was to construct a machine learning model, using routinely collected farm data, to make probabilistic predictions at drying off for an individual cow's risk of a raised somatic cell count (hence intra-mammary infection) post-calving. Anonymized data were obtained as a large convenience sample from 108 UK dairy herds that undertook regular milk recording. The outcome measure evaluated was the presence of a raised somatic cell count in the 30 days post-calving in this observational study. Using a 56-farm training dataset, machine learning analysis was performed using the extreme gradient boosting decision tree algorithm, XGBoost. External validation was undertaken on a separate 28-farm test dataset. Statistical assessment to evaluate model performance using the external dataset returned calibration plots, a Scaled Brier Score of 0.095, and a Mean Absolute Calibration Error of 0.009. Test dataset model calibration performance indicated that the probability of a raised somatic cell count post-calving was well differentiated across probabilities to allow an end user to apply group-level risk decisions. Herd-level new intra-mammary infection rate during the dry period was a key driver of the probability that a cow had a raised SCC post-calving, highlighting the importance of optimizing environmental hygiene conditions. In conclusion, this research has determined that probabilistic classification of the risk of a raised SCC in the 30 days post-calving is achievable with a high degree of certainty, using routinely collected data. These predicted probabilities provide the opportunity for farmers to undertake risk decision-making by grouping cows based on their probabilities and optimizing management strategies for individual cows immediately after calving, according to their likelihood of intra-mammary infection.

An observational study of ear-tagged calf mortality (1 to 100 days) on Irish dairy farms and associations between biosecurity practices and calf mortality on farms participating in a Johne's disease control program.

Postnatal mortality among replacement stock has a detrimental effect on the social, economic, and environmental sustainability of dairy production. Calf mortality rates vary between countries and show differences in temporal trends; most, however, are characterized by high levels of between-farm variability. Explaining this variation can be difficult because herd-level information on management practices relevant to calf health is often not available. The Irish Johne's Control Programme (IJCP) contains a substantial on-farm monitoring program called the Veterinary Risk Assessment and Management Plan (VRAMP). Although this risk assessment is largely focused on factors relevant to the transmission of paratuberculosis, many of its principles are good practice biocontainment policies that are also advocated for the protection of calf health. The objectives of this study were (1) to quantify mortality in ear-tagged Irish dairy calves between 2016 and 2020 using both survival and risk approaches, (2) to determine risk factors for 100-d cumulative mortality hazard in ear-tagged Irish dairy calves between 2016 and 2020, (3) to determine whether 100-d cumulative mortality hazard was higher in ear-tagged calves within herds registered in the IJCP versus those that were not registered in the IJCP and whether there were differences between these cohorts over time, and (4) within IJCP herds, to determine whether VRAMP score or changes in VRAMP score were associated with 100-d cumulative mortality hazard. Excluding perinatal mortality, the overall 100-d cumulative mortality hazard was 4.1%. Calf mortality was consistently underestimated using risk approaches that did not account for calf censoring. Cox proportional hazards models showed that cumulative mortality hazard was greater in male calves; particularly, calves born to Jersey breed dams and those with a beef breed sire. Mortality hazard increased with increasing herd size, was highest in calves born in herds that contract-reared heifers, and lowest in those born in mixed dairy-beef enterprises. Mortality hazard decreased over time with the mortality hazard in 2020 being 0.83 times that of 2016. Mortality hazard was higher in IJCP-registered herds than nonregistered herds (hazard ratio 1.06, 95% CI 1.01-1.12), likely reflecting differences in herds that enrolled in the national program. However, we detected a significant interaction between IJCP status (enrolled vs. not enrolled) and year (hazard ratio 0.96, 95% CI 0.92-1.00), indicating that the decrease in mortality hazard between 2016 and 2020 was greater in IJCP herds versus non-IJCP herds. Finally, increasing VRAMP scores (indicating higher risk for paratuberculosis transmission) were positively associated with increased calf mortality hazard. Postnatal calf mortality rates in Irish dairy herds declined between 2016 and 2020. Our study suggests that implementation of recommended biocontainment practices to control paratuberculosis in IJCP herds was associated with a reduction in calf mortality hazard.

Stability selection for mixed effect models with large numbers of predictor variables: A simulation study.

Covariate selection when the number of available variables is large relative to the number of observations is problematic in epidemiology and remains the focus of continued research. Whilst a variety of statistical methods have been developed to attempt to overcome this issue, at present very few methods are available for wide data that include a clustered outcome. The purpose of this research was to make an empirical evaluation of a new method for covariate selection in wide data settings when the dependent variable is clustered. We used 3300 simulated datasets with a variety of defined structures and known sets of true predictor variables to conduct an empirical evaluation of a mixed model stability selection procedure. Comparison was made with an alternative method based on regularisation using the least absolute shrinkage and selection operator (Lasso) penalty. Model performance was assessed using several metrics including the true positive rate (proportion of true covariates selected in a final model) and false discovery rate (proportion of variables selected in a final model that were non-true (false) variables). For stability selection, the false discovery rate was consistently low, generally remaining ≤ 0.02 indicating that on average fewer than 1 in 50 of the variables selected in a final model were false variables. This was in contrast to the Lasso-based method in which the false discovery rate was between 0.59 and 0.72, indicating that generally more than 60% of variables selected in a final model were false variables. In contrast however, the Lasso method attained higher true positive rates than stability selection, although both methods achieved good results. For the Lasso method, true positive rates remained ≥ 0.93 whereas for stability selection the true positive rate was 0.73-0.97. Our results suggest both methods may be of value for covariate selection with high dimensional data with a clustered outcome. When high specificity is needed for identification of true covariates, stability selection appeared to offer the better solution, although with a slight loss of sensitivity. Conversely when high sensitivity is needed, the Lasso approach may be useful, even if accompanied by a substantial loss of specificity. Overall, the results indicated the loss of sensitivity when employing stability selection is relatively small compared to the loss of specificity when using the Lasso and therefore stability selection may provide the better option for the analyst when evaluating data of this type.

Mastitis Control and Intramammary Antimicrobial Stewardship in Ireland: Challenges and Opportunities.

The Veterinary Medicines Regulation (EU 2019/6) came into force in all EU member states on 28 January 2022. This regulation places particular emphasis on prudent and responsible antimicrobial use in food animal production. Key changes include restrictions on the prophylactic use of antimicrobials in animals, and the possibility to reserve certain antimicrobials for humans only. The Regulation presents challenges to the Irish dairy industry, particularly with respect to current approaches to dry cow therapy. In response, the CellCheck technical working group (TWG, a technical group working in support of CellCheck, the national mastitis control programme) have developed pragmatic national and farm-level recommendations in support of improved mastitis control and intramammary antimicrobial stewardship in the Irish dairy industry. This paper outlines these recommendations, and provides an overview of the evidence considered to inform the TWG during its work (including the Regulation, policy perspectives, international best-practice, international scientific reviews and specific Irish challenges). In many key areas of concern, the TWG recognises the challenges in seeking to shape recommendations in the absence of robust and practical scientific evidence. For this reason, some of the recommended actions are pragmatic in nature, informed by national and international experiences. Periodic programme review will be needed, informed by ongoing monitoring of key performance indicators, to identify those actions that are most effective in an Irish context.

Development of a syndromic surveillance system for Irish dairy cattle using milk recording data.

In the last decade and a half, emerging vector-borne diseases have become a substantial threat to cattle across Europe. To mitigate the impact of the emergence of new diseases, outbreaks must be detected early. However, the clinical signs associated with many diseases may be nonspecific. Furthermore, there is often a delay in the development of new diagnostic tests for novel pathogens which limits the ability to detect emerging disease in the initial stages. Syndromic Surveillance has been proposed as an additional surveillance method that could augment traditional methods by detecting aberrations in non-specific disease indicators. The aim of this study was to develop a syndromic surveillance system for Irish dairy herds based on routinely collected milk recording and meteorological data. We sought to determine whether the system would have detected the 2012 Schmallenberg virus (SBV) incursion into Ireland earlier than conventional surveillance methods. Using 7,743,138 milk recordings from 730,724 cows in 7037 herds between 2007 and 2012, linear mixed-effects models were developed to predict milk yield and alarms generated with temporally clustered deviations from predicted values. Additionally, hotspot spatial analyses were conducted at corresponding time points. Using a range of thresholds, our model generated alarms throughout September 2012, between 4 and 6 weeks prior to the first laboratory confirmation of SBV in Ireland. This system for monitoring milk yield represents both a potentially useful tool for early detection of disease, and a valuable foundation for developing similar tools using other metrics.

Risk factors for detection of bovine viral diarrhoea virus in low-risk herds during the latter stages of Ireland's eradication programme.

BACKGROUND: A national programme to eradicate bovine viral diarrhoea (BVD) has been in place in Ireland since 2013. To inform decision making in the end stages of eradication, and support the development of post-eradication surveillance strategies, an understanding of risks of infection in a low prevalence system is required. METHODS: A case-control study design was implemented. The study population comprised bovine herds that had calves born and tested negative for BVD virus (BVDV) every year from 2013 to 2019 (n = 46,219 herds). We defined cases as herds which had one or more test positive calves for the first time in 2019 (n = 204). Controls (n = 816) were randomly sampled from the herds which remained test negative in 2019. The effects of herd size, management system, inward movements, including those of potential trojan dams (pregnant animals brought into the herd that could potentially be carrying infected calves in utero), and proximity to herds testing positive in the preceding year, were investigated. Network analysis approaches were used to generate variables measuring connections with test positive herds through inward cattle movements. A generalised linear mixed model, including a county-level random effect, was used to explore these risk factors. RESULTS: Our final model retained ln (herd size) (Odds Ratio (95% CI): 1.72 (1.40, 2.12)), distance from test positive herds (0.54 (0.44, 0.66) for each extra land-parcel boundary crossed to reach the closest herd which tested positive the preceding year), and ln (potential trojan dams + 1) (1.29 (1.05, 1.60)). The same variables were retained in the model where herds with confirmed transient infections only (n = 25) were excluded. CONCLUSIONS: Our findings suggest that care with biosecurity at farm boundaries and visitors and equipment entering the farm, and avoidance or careful risk assessment of purchasing potentially pregnant animals, may help prevent introduction of BVDV to low-risk herds. At policy level, consideration of herd size, proximity to test positive herds and purchasing patterns of potentially pregnant cattle may help target surveillance measures towards the end of the eradication programme.

A comparison of the age at first calving of contract-reared versus home-reared replacement dairy heifers.

Recent rapid expansion and increased specialisation of dairy industries internationally has prompted growing interest in collaborative farming enterprises such as contract-rearing of replacement heifers. While contract-rearing provides obvious benefits to dairy farmers intending to expand their herd, it may adversely impact heifer reproductive performance as the farmer no longer manages the heifers on a day-to-day basis. Hence, the objective of this study was to determine if contract-rearing impacted heifer reproductive performance and ultimately achievement of the industry target age at first calving (AFC) of 24 months. Live weight, body condition score (BCS) and morbidity data were collected from 3908 heifers on 99 farms (54 contract-rearing dairy farms; 45 home-rearing dairy farms) on three occasions between one month of age and prior to commencement of their first breeding season; average daily gain (ADG) between farm visits was calculated. Heifer birth dates, breeds, economic breeding index (EBI), mating start dates (MSD) and calving dates were extracted from the national cattle breeding database. To assess the reproductive performance of study heifers, the outcome of interest was age at first calving (AFC). In addition to the effect of heifer rearing system, the association between pre-breeding ADG, body weight and BCS and AFC was also investigated. Descriptive and survival analysis was performed to calculate the distribution of AFC across study farms and to assess the association of rearing strategy and other variables on AFC. Results indicate that there was no difference in median AFC between home and contract-reared heifers (24.4 months). At univariate level, variables significantly associated with AFC were occurrence of pyrexia before MSD, pre-breeding ADG, body weight and BCS. However, when the frailty effect of farm was incorporated into the multivariable model, these variables were no longer significant, indicating wide variation in individual farm management practices not measured in this study. It was concluded that rearing strategy (home versus contract-rearing) did not significantly affect heifer AFC.

The Effect of Contract-Rearing on the Health Status of Replacement Dairy Heifers.

The aim of this study was to compare the health status of contract- vs. conventionally reared replacement dairy heifers over a 2-year period. A total of 120 dairy farmers were enrolled in the study in spring 2018: 55 farmers were rearing their own heifers (control farmers; CFs), and 65 were sending heifers to a contract-rearing farm (source dairy farmers; SDFs). Between spring 2018 and autumn 2019, approximately 5500 replacement heifers from these farms were monitored for signs of ill health during four farm visits using a modified version of the Wisconsin calf health scoring system. Additionally, faecal and nasal swabs were taken from a proportion of heifers with clinical signs of diarrhoea and respiratory disease to determine the associated aetiological agents. Results indicate few differences in the health status and pathogen exposure status of home-reared versus contract-reared heifers. Additionally, the number of source dairy farms represented and commingling of heifers from multiple origins at the rearing unit were not associated with an increased incidence of respiratory disease or diarrhoea among contract-reared heifers. It was concluded that contract-rearing did not result in adverse health outcomes for replacement dairy heifers. This is the first study to demonstrate this finding in a robust, longitudinal, herd-level population study.

Longitudinal Prevalence of Antibodies to Endemic Pathogens in Bulk Tank Milk Samples From Dairy Herds Engaged or Not in Contract Heifer Rearing.

Since the abolition of EU milk production quotas in 2015, Europe's dairy industries have undergone a period of rapid expansion with possible resultant increased inter-herd transmission of endemic pathogens. The aims of this study were (1) to establish the post-2015 prevalence of antibodies to selected endemic infectious diseases and (2) to determine if prevalences differed between herds where heifers were reared at home and those where heifers were sent out for contract-rearing. Three bulk tank milk (BTM) samples were collected annually between May and August of 2018-20 inclusively from 120 Irish dairy herds. Additionally, herd vaccination status was collected by questionnaire. Milk samples were tested using commercially available ELISAs for eight pathogens: bovine viral diarrhea virus (BVDV), bovine herpesvirus 1 (BoHv-1), bovine respiratory syncytial virus (BRSV), Mycoplasma bovis, Mycobacterium avium subspecies paratuberculosis (MAP), Salmonella Dublin (S. Dublin), Leptospira Hardjo (L. Hardjo), and Neospora caninum (N. caninum). The true prevalence of each pathogen was calculated using a Rogan-Gladen estimator. The true prevalences (95% CI) of BTM antibodies in unvaccinated herds across the 3 years were as follows (i) BVDV: 57, 86, and 73% (95% CI: 40.7-65.9, 74-94, and 58-85) (n = 56, 56, and 48), (ii) BoHv-1: 47, 49, and 19% (95% CI: 26.3-69.7, 25-75, and 1-56) (n = 21, 20, and 11), (iii) L. Hardjo: 34, 59, and 73% (95% CI: 12.5-63, 33-82, and 33-99) (n = 15, 21, and 10), (iv) S. Dublin 32, 57, and 11% (95% CI: 12.21-68.1, 30.2-90.1, and 0) (n = 19, 22, and 13), (v) BRSV: 100% (95% CI: 99.5-100, 100, and 100) (n = 120, 109, and 91), (vi) MAP: 0% (95% CI: 0, 0, and 0) (n = 120, 109, and 91) (vii) N. caninum 0% (95% CI: 0, 0, and 0) (n = 120, 109, and 91) and (viii) M. bovis (ELISA) 53, 0.42, and 30% (95% CI: 3.95-6.84, 0, and 21-41) (n = 120, 109, and 91). M. bovis was detected by PCR in 0, 1, and 0% of herds in 2018, 2019, and 2020, respectively. This study showed that expanding Irish dairy herds are endemically infected with several of the studied pathogens. No differences in herd prevalence of infectious agents were observed between farms with different heifer rearing strategies (contract-rearing vs. traditional rearing).

Numbers of close contacts of individuals infected with SARS-CoV-2 and their association with government intervention strategies.

BACKGROUND: Contact tracing is conducted with the primary purpose of interrupting transmission from individuals who are likely to be infectious to others. Secondary analyses of data on the numbers of close contacts of confirmed cases could also: provide an early signal of increases in contact patterns that might precede larger than expected case numbers; evaluate the impact of government interventions on the number of contacts of confirmed cases; or provide data information on contact rates between age cohorts for the purpose of epidemiological modelling. We analysed data from 140,204 close contacts of 39,861 cases in Ireland from 1st May to 1st December 2020. RESULTS: Negative binomial regression models highlighted greater numbers of contacts within specific population demographics, after correcting for temporal associations. Separate segmented regression models of the number of cases over time and the average number of contacts per case indicated that a breakpoint indicating a rapid decrease in the number of contacts per case in October 2020 preceded a breakpoint indicating a reduction in the number of cases by 11 days. CONCLUSIONS: We found that the number of contacts per infected case was overdispersed, the mean varied considerable over time and was temporally associated with government interventions. Analysis of the reported number of contacts per individual in contact tracing data may be a useful early indicator of changes in behaviour in response to, or indeed despite, government restrictions. This study provides useful information for triangulating assumptions regarding the contact mixing rates between different age cohorts for epidemiological modelling.

Diagnosis of respiratory disease in preweaned dairy calves using sequential thoracic ultrasonography and clinical respiratory scoring: Temporal transitions and association with growth rates.

Bovine respiratory disease (BRD) in dairy calves is a multifactorial condition, involving environmental, host, and pathogen factors. Thoracic ultrasound scoring (TUS) has recently been validated as an accurate method of detecting BRD-related lung pathology in dairy calves. Previous studies investigating the use of TUS in preweaned dairy calves have largely been based on cross-sectional data from all-year production systems. The objectives of this longitudinal observational study were to characterize the temporal transitions in TUS scores in dairy calves from pasture-based, seasonal-calving herds using sequential examinations during the preweaning period, and to investigate the relationship between the presence and temporal pattern of BRD, diagnosed by TUS or clinical respiratory scoring (CRS), and average daily gain (ADG). In spring of 2019, 317 preweaned calves from 7 commercial dairy farms were recruited at less than 4 wk old (ranging from 1-27 d of age). Each farm was examined on at least 3 occasions at 20- to 28-d intervals and housed indoors in group or individual pens. At each visit TUS scores, CRS scores based on the University of Wisconsin Calf Respiratory Score Chart (https://www.vetmed.wisc.edu/fapm/wp-content/uploads/2020/01/calf_respiratory_scoring_chart.pdf), and live weight using a dairy breed-specific weigh band were recorded. All data were recorded by the same 2 veterinarians over the course of the study. The final data set consisted of 966 TUS and CRS scores collected from 317 calves over a period of approximately 6 wk from 7 farms. The data were analyzed in multivariable, mixed effects, linear regression models, with separate models constructed for TUS and CRS scores. Random effects (intercepts) were included for calf, farm, and visit week. Additionally, a random slope was included for age at sampling by farm. Median farm TUS score ranged from 0 to 2.5 over the 3 visits (possible range: 0-5). The percentage of calves with a TUS score ≥3 (consolidation of the full thickness of 1 lung lobe), on each farm ranged from 0 to 50%. The median CRS in calves on individual farms ranged from 1 to 3 over the 3 visits (possible range: 0-12). The percentage of calves on each farm with a CRS score ≥5 (possible range: 0-12) ranged from 0 to 26%. The TUS and CRS scores were weakly correlated. The TUS was associated with reduced ADG. Calves with TUS scores ≥3 grew at 126 g/d less than unaffected calves over the 3-wk period before examination. The predicted effect on ADG was dependent on the age and duration over which the animal was affected. Calves affected later (i.e., between visits 2 and 3) had lower predicted weights at 63 d compared with calves with increased TUS scores earlier in the study period. Calves with a TUS score ≥3 at each of the 3 sampling points had the lowest weight at 63 d of age. There was no association of CRS with ADG. This study showed that in contrast to CRS, higher TUS scores are associated with lower ADG, with weight loss being more pronounced in chronic cases.

Estimation of the serial interval and proportion of pre-symptomatic transmission events of COVID- 19 in Ireland using contact tracing data.

BACKGROUND: The serial interval is the period of time between the onset of symptoms in an infector and an infectee and is an important parameter which can impact on the estimation of the reproduction number. Whilst several parameters influencing infection transmission are expected to be consistent across populations, the serial interval can vary across and within populations over time. Therefore, local estimates are preferable for use in epidemiological models developed at a regional level. We used data collected as part of the national contact tracing process in Ireland to estimate the serial interval of SARS-CoV-2 infection in the Irish population, and to estimate the proportion of transmission events that occurred prior to the onset of symptoms. RESULTS: After data cleaning, the final dataset consisted of 471 infected close contacts from 471 primary cases. The median serial interval was 4 days, mean serial interval was 4.0 (95% confidence intervals 3.7, 4.3) days, whilst the 25th and 75th percentiles were 2 and 6 days respectively. We found that intervals were lower when the primary or secondary case were in the older age cohort (greater than 64 years). Simulating from an incubation period distribution from international literature, we estimated that 67% of transmission events had greater than 50% probability of occurring prior to the onset of symptoms in the infector. CONCLUSIONS: Whilst our analysis was based on a large sample size, data were collected for the primary purpose of interrupting transmission chains. Similar to other studies estimating the serial interval, our analysis is restricted to transmission pairs where the infector is known with some degree of certainty. Such pairs may represent more intense contacts with infected individuals than might occur in the overall population. It is therefore possible that our analysis is biased towards shorter serial intervals than the overall population.

Cow-level prevalence and risk factors for estrus detection inaccuracy in seasonal calving pasture-based dairy cows.

High submission rates and pregnancies per AI are essential to ensure compact calving is achieved in seasonal calving pasture-based systems. Estrus detection inaccuracy (EDI) is one area that negatively impacts pregnancies per AI as it increases the inseminations per pregnancy with little probability of conception, while also having the potential to disrupt established pregnancies. The aims of this cross-sectional study were to provide cow-level estimates of EDI prevalence and determine cow-level risk factors for EDI in seasonal calving pasture-based systems. A total of 1071 milk samples were obtained from 984 cows on 19 farms in spring 2018 and analyzed by radioimmunoassay to determine the progesterone concentration at the time of artificial insemination. Based on a validation study on a subset of cows, an inaccurate estrus detection was described as a concentration of progesterone in foremilk of ≥3 ng/ml which corresponded to a composite milk progesterone value of 5 ng/ml. To investigate selected risk factors for EDI, we conducted statistical analyses using two multivariate logistic regression models, stratifying by insemination number (first versus repeat). The overall prevalence of EDI was 4.7% with a prevalence of 3.3% of EDI at first insemination and 14.1% at repeat insemination. Absence of a mounting abrasion (Adjusted odds ratio (AOR) = 3.0) was a significant risk factor for EDI on first insemination while abnormal preceding repeat interval (AOR = 9.5), the absence of an observed standing estrus (AOR = 12.5) and the absence of a mounting abrasion (AOR = 4.1) were significant risk factors for EDI on repeat insemination. The results indicate that cow-level estimated prevalence of EDI in a selection of pasture-based herds was low at first insemination but higher for repeat insemination. It confirms that certain cow-level risk factors existed for EDI, thus providing preliminary evidence for potential future investigation into the targeted use of on-farm progesterone assays in pasture-based herds.

Incubation period of COVID-19: a rapid systematic review and meta-analysis of observational research.

OBJECTIVES: The aim of this study was to conduct a rapid systematic review and meta-analysis of estimates of the incubation period of COVID-19. DESIGN: Rapid systematic review and meta-analysis of observational research. SETTING: International studies on incubation period of COVID-19. PARTICIPANTS: Searches were carried out in PubMed, Google Scholar, Embase, Cochrane Library as well as the preprint servers MedRxiv and BioRxiv. Studies were selected for meta-analysis if they reported either the parameters and CIs of the distributions fit to the data, or sufficient information to facilitate calculation of those values. After initial eligibility screening, 24 studies were selected for initial review, nine of these were shortlisted for meta-analysis. Final estimates are from meta-analysis of eight studies. PRIMARY OUTCOME MEASURES: Parameters of a lognormal distribution of incubation periods. RESULTS: The incubation period distribution may be modelled with a lognormal distribution with pooled mu and sigma parameters (95% CIs) of 1.63 (95% CI 1.51 to 1.75) and 0.50 (95% CI 0.46 to 0.55), respectively. The corresponding mean (95% CIs) was 5.8 (95% CI 5.0 to 6.7) days. It should be noted that uncertainty increases towards the tail of the distribution: the pooled parameter estimates (95% CIs) resulted in a median incubation period of 5.1 (95% CI 4.5 to 5.8) days, whereas the 95th percentile was 11.7 (95% CI 9.7 to 14.2) days. CONCLUSIONS: The choice of which parameter values are adopted will depend on how the information is used, the associated risks and the perceived consequences of decisions to be taken. These recommendations will need to be revisited once further relevant information becomes available. Accordingly, we present an R Shiny app that facilitates updating these estimates as new data become available.

Individual and herd-level milk ELISA test status for Johne's disease in Ireland after correcting for non-disease-associated variables.

Antibody-detecting tests for Mycobacterium avium ssp. paratuberculosis (MAP) have low sensitivity and imperfect specificity for detection of infection. Sensitivity increases as the disease progresses. Aside from infection status and stage of disease, several factors affect test performance. These factors have not yet been studied in dairy cows producing lower volumes of milk with higher solids concentration, such as those managed in low-input, pasture-based production systems. Furthermore, the effect of correcting for these associations on individual and herd test status is also unknown. The first objective of this study was to examine the relationship between MAP antibody response in milk and milk yield, somatic cell count (SCC), fat and protein contents, and stage of lactation in dairy cows enrolled in the national Johne's Disease Control Programme (JDCP) in Ireland. The second objective was to examine the effect of correcting the antibody response for these associations on the test status of individual cows and herds, given that individual tests are often used to define a herd's status. Data were extracted for herds in the JDCP from January 2014 to December 2015 inclusive, consisting of 42,657 milk recordings from 18,569 cows across 187 dairy herds. Two linear regression models were constructed to investigate the association between log-transformed MAP sample-to-positive ratio and milk recording data and in primi- and multiparous cows. Days in milk was modeled as a B-spline in each model, and cow and herd were included as random effects. Across both models, natural log-transformed MAP antibody response was negatively associated with milk yield, positively associated with protein and fat production, and had a curvilinear association with log-transformed SCC. The association between MAP antibody response and days in milk varied over the course of the lactation. However, when combined, these variables explained only 5.1% of the variation in the antibody response of the population. After correcting for these associations, 93 multiparous cows and 20 primiparous cows changed category (negative, suspect, or positive). When considered at the herd-test level, out of a total of 531 herd tests, 1 herd changed from negative to positive, and 5 herds changed from positive to negative. This study provides useful information to aid in the interpretation of antibody results for herds testing animals for the presence of MAP infection. At an overall population level, correction of the serological response for non-disease-associated factors has the potential to change the status of only a small number of cows. At the herd level, the proportion of herds changing status was minimal. However, depending on the implications of a herd-level serological diagnosis, consideration should be given to correcting for these non-disease-associated variables within the context of national JD control programs.

Using examination performance data and focus groups to inform teaching - a case study from final year students of veterinary medicine.

BACKGROUND: Student feedback has played an important role in the maintenance of quality and standards in higher education. Perhaps the most commonly used method to capture feedback is a series of questions or statements where students indicate their degree of satisfaction or agreement. Focus groups offer an alternative means of capturing 'richer' qualitative data relating to students' thoughts on course structure. Aside from student evaluations, student examination performance has been used as a method to evaluate the efficacy of curriculum changes at programme level. However, this data is utilised less so at a 'finer detail' level to identify specific issues with the delivery of teaching. CASE PRESENTATION: The purpose of this report was to outline the approach taken using qualitative and quantitative data to identify problems with a specific area of teaching, inform a new teaching approach and to assess the impact of those changes. Following quantitative and qualitative analysis, a practical class on dairy herd fertility performance was highlighted as an area for improvement. After the introduction of the newly formatted practical class with a greater focus on self-directed learning, there was a significant increase in the average score (p < 0.001) and a decrease in the proportion of students failing (p < 0.001) the question that assessed the analysis of dairy herd fertility data. In addition, the R-squared value between students' performance in the fertility question and their performance in the overall examination increased from 0.06 to 0.11. CONCLUSIONS: The combination of qualitative focus group data and quantitative analysis of examination performance data represent robust methods for identifying problems associated with specific aspects of veterinary teaching.

Relative effect of milk constituents on fertility performance of milk-recorded, spring-calving dairy cows in Ireland.

Several studies have reported associations between milk composition data and fertility performance. However, no work to date has estimated the effect of milk constituents on fertility performance in cows with low milk constituent concentrations. The objective of this study was to assess the association between milk constituents, animal characteristics, and time from mating start date (MSD) to conception using survival analysis. Furthermore, we aimed to investigate the relative effect of each variable by predicting median times to conception for animals with different combinations of characteristics and milk compositions. The final data set consisted of 87,227 cow lactation records from 64,519 cows in 2,049 herds with calving dates from 2010 to 2013. Milk recording data from each lactation were used, including test day recordings at 0 to 30, 31 to 60, and 61 to 90 d in milk (DIM). The analysis was limited to spring-calving cows (i.e., animals calving from January to May inclusive). Mating start date was determined for each unique herd in each year. A cow-specific MSD (MSDcow) was defined taking into consideration the MSD for each herd and the calving date and a minimum calving to insemination interval of each herd year. The conception date for each cow was estimated using the subsequent calving date. Cows with no subsequent calving date were assumed not to have conceived. Time from MSDcow to approximate conception date was analyzed using survival analysis. Cox proportional hazard models were constructed for each of the 3 recording windows: 0 to 30, 31 to 60, and 61 to 90 DIM. A fourth model was used to assess the dynamics of milk composition over the 3 windows. To investigate the effect of these variables, model outputs were used to create parametric accelerated failure time models to predict median survival times for animals at the 10th and 90th percentiles of the variable of interest but otherwise identical across the rest of the variables. Results demonstrated that fertility breeding subindex had the largest effect on time from MSDcow to conception, with an additional 62 d open for those in the 10th percentile versus those in the 90th percentile. Of the milk constituents, milk lactose concentration had the greatest effect on MSD to conception, particularly when measured from 0 to 30 DIM. An additional 10 d open resulted from comparing those in the 10th and 90th percentiles. Milk protein concentration, although statistically significant, had a lower effect on fertility outcome when comparing cows in the 10th and 90th percentiles for this exposure variable. The greatest effect was found in the 61 to 90 DIM recording window, where cows in the 10th percentile had an additional 9 d open at the subsequent breeding season compared with those in the 90th percentile. Overall, our study shows that although the associations between milk constituents and fertility are statistically significant, their overall influence in determining MSD to conception in this study population is relatively modest, particularly compared with fertility breeding subindex, when comparing cows at the 10th and 90th percentiles. Of the milk constituents measured, milk lactose concentration measured at 0 to 30 DIM had the greatest effect in determining fertility outcome when comparing cows at the 10th and 90th percentiles. The predictive value of early-lactation test day milk composition data on hazard of pregnancy during the following breeding period, within a spring-calving context, appears to be relatively modest at the individual-cow level. Further work is required to test the usefulness of these associations at the herd level.