Salmonella in Swedish cattle

In Sweden, all herds detected with salmonella are put under restrictions and measures aiming at eradication are required. The purpose of these studies was to provide a basis for decisions on how surveillance and control of salmonella in Swedish cattle can be made more cost-efficient. Results from a bulk milk screening were used to investigate seroprevalence of salmonella and to study associations between salmonella status and geographical location, local animal density, number of test positive neighbour herds, animal trade and herd size. Additional information on potential risk factors for salmonella was collected via a questionnaire sent to selected herds. The results confirmed a low prevalence of salmonella in Swedish dairy herds throughout the country, except for an island in the southeast (Öland). Test-positive salmonella status was associated with test-positive neighbours, with a stronger association for herds with indication of infection with the host-adapted S. Dublin, than for those with indication of infection with other serotypes. The results suggest local spread as an important component in transmission of salmonella between herds. Specific factors of importance in this local spread were not identified, suggesting that a broad biosecurity approach is needed in prevention of salmonella. Infection with S. Dublin was associated with herd size, and herd size was in turn associated with type of housing and many management factors, which might affect the persistence of salmonella in a herd. Costs for implementation of required measures in restricted herds during the years 1999-2013 were on average 0.49 million EUR per farm, with a median of 0.11 EUR, and a range of 1080 EUR to 4.44 million EUR. Larger herds and longer restriction periods were associated with higher costs. Efficiency of different sampling strategies was evaluated on herd level. The study highlights the importance of considering a herd’s risk of having salmonella when deciding on sampling strategies for different purposes, e.g. surveillance of pre-purchase testing

A modelling framework for the prediction of the herd-level probability of infection from longitudinal data

For many infectious diseases of farm animals, there exist collective control programmes (CPs) that rely on the application of diagnostic testing at regular time intervals for the identification of infected animals or herds. The diversity of these CPs complicates the trade of animals between regions or countries because the definition of freedom from infection differs from one CP to another. In this paper, we describe a statistical model for the prediction of herd level probabilities of infection from longitudinal data collected as part of CPs against infectious diseases of cattle. The model was applied to data collected as part of a CP against infections by the bovine viral diarrhoea virus (BVDV) in Loire-Atlantique, France. The model represents infection as a herd latent status with a monthly dynamics. This latent status determines test results through test sensitivity and test specificity. The probability of becoming status positive between consecutive months is modelled as a function of risk factors (when available) using logistic regression. Modelling is performed in a Bayesian framework. Prior distributions need to be provided for the sensitivities and specificities of the different tests used, for the probability of remaining status positive between months as well as for the probability of becoming positive between months. When risk factors are available, prior distributions need to be provided for the coefficients of the logistic regression in place of the prior for the probability of becoming positive. From these prior distributions and from the longitudinal data, the model returns posterior probability distributions for being status positive in all herds on the current months. Data from the previous months are used for parameter estimation. The impact of using different prior distributions and model settings on parameter estimation was evaluated using the data. The main advantage of this model is its ability to predict a probability of being status positive on a month from inputs that can vary in terms of nature of test, frequency of testing and risk factor availability. The main challenge in applying the model to the BVDV CP data was in identifying prior distributions, especially for test characteristics, that corresponded to the latent status of interest, i.e. herds with at least one persistently infected (Pl) animal. The model is available on Github as an R packag

A modelling framework for the prediction of the herd-level probability of infection from longitudinal data

For many infectious diseases of farm animals, there exist collective control programmes (CPs) that rely on the application of diagnostic testing at regular time intervals for the identification of infected animals or herds. The diversity of these CPs complicates the trade of animals between regions or countries because the definition of freedom from infection differs from one CP to another. In this paper, we describe a statistical model for the prediction of herd level probabilities of infection from longitudinal data collected as part of CPs against infectious diseases of cattle. The model was applied to data collected as part of a CP against infections by the bovine viral diarrhoea virus (BVDV) in Loire-Atlantique, France. The model represents infection as a herd latent status with a monthly dynamics. This latent status determines test results through test sensitivity and test specificity. The probability of becoming status positive between consecutive months is modelled as a function of risk factors (when available) using logistic regression. Modelling is performed in a Bayesian framework. Prior distributions need to be provided for the sensitivities and specificities of the different tests used, for the probability of remaining status positive between months as well as for the probability of becoming positive between months. When risk factors are available, prior distributions need to be provided for the coefficients of the logistic regression in place of the prior for the probability of becoming positive. From these prior distributions and from the longitudinal data, the model returns posterior probability distributions for being status positive in all herds on the current months. Data from the previous months are used for parameter estimation. The impact of using different prior distributions and model settings on parameter estimation was evaluated using the data. The main advantage of this model is its ability to predict a probability of being status positive on a month from inputs that can vary in terms of nature of test, frequency of testing and risk factor availability. The main challenge in applying the model to the BVDV CP data was in identifying prior distributions, especially for test characteristics, that corresponded to the latent status of interest, i.e. herds with at least one persistently infected (Pl) animal. The model is available on Github as an R packag

A modelling framework for the prediction of herd-level probability of infection from longitudinal data

The collective control programmes (CPs) that exist for many infectious diseases of farmanimals rely on the application of diagnostic testing at regular time intervals for theidentification of infected animals or herds. The diversity of these CPs complicates thetrade of animals between regions or countries because the definition of freedom frominfection differs from one CP to another. In this paper, we describe a statistical model forthe prediction of herd-level probabilities of infection from longitudinal data collected aspart of CPs against infectious diseases of cattle. The model was applied to data collectedas part of a CP against bovine viral diarrhoea virus (BVDV) infection in Loire-Atlantique,France. The model represents infection as a herd latent status with a monthly dynamics.This latent status determines test results through test sensitivity and test specificity. Theprobability of becoming status positive between consecutive months is modelled as afunction of risk factors (when available) using logistic regression. Modelling is performedin a Bayesian framework, using either Stan or JAGS. Prior distributions need to be providedfor the sensitivities and specificities of the different tests used, for the probability ofremaining status positive between months as well as for the probability of becomingpositive between months. When risk factors are available, prior distributions need to beprovided for the coefficients of the logistic regression, replacing the prior for the probabilityof becoming positive. From these prior distributions and from the longitudinal data, themodel returns posterior probability distributions for being status positive for all herds onthe current month. Data from the previous months are used for parameter estimation.The impact of using different prior distributions and model implementations on parameterestimation was evaluated. The main advantage of this model is its ability to predict aprobability of being status positive in a month from inputs that can vary in terms of natureof test, frequency of testing and risk factor availability/presence. The main challenge inapplying the model to the BVDV CP data was in identifying prior distributions, especiallyfor test characteristics, that corresponded to the latent status of interest, i.e. herds withat least one persistently infected (PI) animal. The model is available on Github as an Rpackage (https://github.com/AurMad/STOCfree) and can be used to carry out output-basedevaluation of disease CPs