Current value of historical and ongoing surveillance for disease freedom: surveillance for bovine Johne's disease in Western Australia

‘Confidence’ in freedom from disease is generally derived from multiple sources of varied surveillance information, and typically this surveillance evidence has been accumulated over time. In the state of Western Australia (WA) the main surveillance evidence supporting Free Zone status in the national bovine Johne's disease (BJD) program comprises periodic surveys and the ongoing clinical diagnostic system. This paper illustrates a simple approach to current valuation of historical surveillance information, based on the calculated sensitivity of the surveillance processes, the time elapsed since the data were accumulated, and the probability of new introduction of disease into the population during that elapsed time. Surveillance system components (SSCs) contributing to the overall sensitivity of the surveillance system were the clinical diagnostic system and periodic targeted surveys. Sensitivity of each component was estimated using a stochastic scenario tree model of the surveillance process as implemented. Probability of introduction of BJD into WA during each time period was estimated retrospectively from a stochastic import risk analysis model applied to actual cattle importation data. The probability that the WA cattle population was free from infection (at design prevalences of 0.2% of herds and 2% of animals within an infected herd) was estimated following each of 11 years, giving a median probability that the State was free of BJD (at these design prevalences) at the end of 2005 of 0.89. The meaning of this result is discussed.     

Adapting a scenario tree model for freedom from disease as surveillance progresses: The Canadian notifiable avian influenza model

Scenario tree models with temporal discounting have been applied in four continents to support claims of freedom from animal disease. Recently, a second (new) model was developed for the same population and disease. This is a natural development because surveillance is a dynamic process that needs to adapt to changing circumstances – the difficulty is the justification for, documentation of, presentation of and the acceptance of the changes.     

Balancing diets for physically effective fibre and ruminally degradable starch: A key to lower the risk of sub-acute rumen acidosis and improve productivity of dairy cattle

Finding an optimal balance between the amount of physically effective fibre and rumen fermentable carbohydrates in the diets of high-producing dairy cows is difficult, but critical to prevent sub-acute ruminal acidosis (SARA), optimize digestion, nutrient utilization, and improve productivity. The main aim of the present review was to provide a summary of recent achievements regarding the role that interactions between dietary factors such as grain fermentability and physically effective neutral detergent fibre (peNDF) have on rumen fermentation and SARA as well as on production performance in high-producing dairy cows. The review first considers factors related to grain fermentability and finds that the latter variable depends mainly on the source of starch as well as in processing and conservation method; however, dairy cow's response to grain fermentability should be considered in relation with the total amount of starch and/or concentration of peNDF in the diet. Forages and current methods used in the evaluation of their physical characteristics were also reviewed suggesting the use of Penn State Particle Separator as an adequate approach to determine the distribution of particle fractions as well as the content of peNDF in both forages and total mixed rations. Furthermore, results reviewed in this study suggest that a content of peNDF_> 1.18 (expressed inclusive of particles-dry matter > 1.18 mm) in the diet of about 30–32% is sufficient in maintaining a daily mean of ruminal pH of 6.2, lowering the risk of SARA, and preventing milk fat depression without exerting any negative effects on daily feed intake and milk production. The review concludes with a practical recommendation in terms of balancing diets with peNDF_> 1.18 and ruminally degradable starch, suggesting that a ratio between the two variables lower than 1.45 should be avoided when formulating diets for lactating dairy cows.     

Probability of freedom from disease after the first detection and eradication of PRRS in Sweden: Scenario-tree modelling of the surveillance system

In July 2007, PRRS was detected for the first time in Sweden. A total of eight positive herds were identified and various measures were taken to eradicate the disease, including restrictions and slaughter of infected herds. Subsequently, both active and passive surveillance activities were undertaken. This study describes stochastic scenario-tree modelling of all the various surveillance system components, to estimate the current probability that Sweden is free from PRRS. The model includes all actions taken after the first positive herd was detected. The surveillance system components included in the model were as follows: investigations undertaken in association with the outbreak, a serological study based on samples collected at slaughter, samples collected in the national PRRS surveillance programme and passive clinical surveillance. The probability of freedom was estimated in time steps of 1 month, from July to December 2007. After each time step, the calculated posterior probability of freedom from the previous month, combined with the probability of introduction, was used as a prior probability for the next month.The result from the model showed a 99.8% probability that Sweden was free from PRRS at the end of December 2007. The estimated total sensitivity of the surveillance system varied between 81.2% and 94.3% and was highest during the first months after the outbreak. For sensitivity analysis purposes, the model was also applied using higher risks of introduction. However, this did not make considerable difference to the final estimates.     

Bayesian evaluation of budgets for endemic disease control: An example using management changes to reduce milk somatic cell count early in the first lactation of Irish dairy cows

The aim of this research was to determine budgets for specific management interventions to control heifer mastitis in Irish dairy herds as an example of evidence synthesis and 1-step Bayesian micro-simulation in a veterinary context. Budgets were determined for different decision makers based on their willingness to pay. Reducing the prevalence of heifers with a high milk somatic cell count (SCC) early in the first lactation could be achieved through herd level management interventions for pre- and peri-partum heifers, however the cost effectiveness of these interventions is unknown. A synthesis of multiple sources of evidence, accounting for variability and uncertainty in the available data is invaluable to inform decision makers around likely economic outcomes of investing in disease control measures. One analytical approach to this is Bayesian micro-simulation, where the trajectory of different individuals undergoing specific interventions is simulated. The classic micro-simulation framework was extended to encompass synthesis of evidence from 2 separate statistical models and previous research, with the outcome for an individual cow or herd assessed in terms of changes in lifetime milk yield, disposal risk, and likely financial returns conditional on the interventions being simultaneously applied. The 3 interventions tested were storage of bedding inside, decreasing transition yard stocking density, and spreading of bedding evenly in the calving area. Budgets for the interventions were determined based on the minimum expected return on investment, and the probability of the desired outcome. Budgets for interventions to control heifer mastitis were highly dependent on the decision maker's willingness to pay, and hence minimum expected return on investment. Understanding the requirements of decision makers and their rational spending limits would be useful for the development of specific interventions for particular farms to control heifer mastitis, and other endemic diseases.     

Risk Prioritization Tool to Identify the Public Health Risks of Wildlife Trade: The Case of Rodents from Latin America

Wildlife trade (both formal and informal) is a potential driver of disease introduction and emergence. Legislative proposals aim to prevent these risks by banning wildlife imports, and creating ‘white lists’ of species that are cleared for importation. These approaches pose economic harm to the pet industry, and place substantial burden on importers and/or federal agencies to provide proof of low risk for importation of individual species. As a feasibility study, a risk prioritization tool was developed to rank the pathogens found in rodent species imported from Latin America into the United States with the highest risk of zoonotic consequence in the United States. Four formally traded species and 16 zoonotic pathogens were identified. Risk scores were based on the likelihood of pathogen release and human exposure, and the severity of the disease (consequences). Based on the methodology applied, three pathogens (Mycobacterium microti, Giardia spp. and Francisella tularensis) in one species (Cavia porcellus) were ranked as highest concern. The goal of this study was to present a methodological approach by which preliminary management resources can be allocated to the identified high‐concern pathogen–species combinations when warranted. This tool can be expanded to other taxa and geographic locations to inform policy surrounding the wildlife trade.     

Risk factors for bovine respiratory disease in Australian feedlot cattle: Use of a causal diagram-informed approach to estimate effects of animal mixing and movements before feedlot entry

A nationwide longitudinal study was conducted to investigate risk factors for bovine respiratory disease (BRD) in cattle in Australian feedlots. After induction (processing), cattle were placed in feedlot pens (cohorts) and monitored for occurrence of BRD over the first 50 days on feed. Data from a national cattle movement database were used to derive variables describing mixing of animals with cattle from other farms, numbers of animals in groups before arrival at the feedlot, exposure of animals to saleyards before arrival at the feedlot, and the timing and duration of the animal's move to the vicinity of the feedlot. Total and direct effects for each risk factor were estimated using a causal diagram-informed process to determine covariates to include in four-level Bayesian logistic regression models. Mixing, group size and timing of the animal's move to the feedlot were important predictors of BRD. Animals not mixed with cattle from other farms prior to 12 days before induction and then exposed to a high level of mixing (≥4 groups of animals mixed) had the highest risk of developing BRD (OR 3.7) compared to animals mixed at least 4 weeks before induction with less than 4 groups forming the cohort. Animals in groups formed at least 13 days before induction comprising 100 or more (OR 0.5) or 50–99 (OR 0.8) were at reduced risk compared to those in groups of less than 50 cattle. Animals moved to the vicinity of the feedlot at least 27 days before induction were at reduced risk (OR 0.4) compared to cattle undergoing short-haul transportation (<6 h) to the feedlot within a day of induction, while those experiencing longer transportation durations (6 h or more) within a day of induction were at slightly increased risk (OR 1.2). Knowledge of these risk factors could potentially be used to inform management decisions to reduce the risk of BRD in feedlot cattle.     

The use of Markov chain Monte Carlo for analysis of correlated binary data: patterns of somatic cells in milk and the risk of clinical mastitis in dairy cows

Two analytical approaches were used to investigate the relationship between somatic cell concentrations in monthly quarter milk samples and subsequent, naturally occurring clinical mastitis in three dairy herds. Firstly, cows with clinical mastitis were selected and a conventional matched analysis was used to compare affected and unaffected quarters of the same cow. The second analysis included all cows, and in order to overcome potential bias associated with the correlation structure, a hierarchical Bayesian generalised linear mixed model was specified. A Markov chain Monte Carlo (MCMC) approach, that is Gibbs sampling, was used to estimate parameters.

The results of both the matched analysis and the hierarchical modelling suggested that quarters with a somatic cell count (SCC) in the range 41,000–100,000 cells/ml had a lower risk of clinical mastitis during the next month than quarters <41,000 cell/ml. Quarters with an SCC >200,000 cells/ml were at the greatest risk of clinical mastitis in the next month. There was a reduced risk of clinical mastitis between 1 and 2 months later in quarters with an SCC of 81,000–150,000 cells/ml compared with quarters below this level. The hierarchical modelling analysis identified a further reduced risk of clinical mastitis between 2 and 3 months later in quarters with an SCC 61,000–150,000 cells/ml, compared to other quarters.

We conclude that low concentrations of somatic cells in milk are associated with increased risk of clinical mastitis, and that high concentrations are indicative of pre-existing immunological mobilisation against infection. The variation in risk between quarters of affected cows suggests that local quarter immunological events, rather than solely whole cow factors, have an important influence on the risk of clinical mastitis. MCMC proved a useful tool for estimating parameters in a hierarchical Bernoulli model. Model construction and an approach to assessing goodness of model fit are described.