Combining Salmonella Dublin genome information and contact-tracing to substantiate a new approach for improved detection of infectious transmission routes in cattle populations.

This study presents a new method for detection of between-herd livestock movements to facilitate disease tracing and more accurately describe network behaviour of relevance for spread of infectious diseases, including within-livestock business risk-carrying contacts that are not necessarily recorded anywhere. The study introduces and substantiates the concept of grouping livestock herds into business-units based on ownership and location in the tracing analysis of animal movement-based contact networks. To test the utility of this approach, whole core genome sequencing of 196 Salmonella Dublin isolates stored from previous surveillance and project activities was combined with information on cattle movements recorded in the Danish Cattle Database between 1997 and 2017. The aim was to investigate alternative explanations for S. Dublin circulation in groups of herds connected by ownership, but without complete records of livestock movements. The EpiContactTrace R-package was used to trace the contact networks between businesses and compare the network characteristics of businesses sharing strains of S. Dublin with different levels of genetic relatedness. The ownership-only definition proved to be an unreliable grouping approach for large businesses, which could have internal distances larger than 250 km and therefore do not represent useful epidemiological units. Therefore, the grouping was refined using spatial analysis. More than 90% of final business units formed were composed of one single cattle property, whereas multi-property businesses could reach up to eight properties in a given year, with up to 15 cattle herds having been part of the same business through the study period. Results showed markedly higher probabilities of introduction of infectious animals between proposed businesses from which the same clone of S. Dublin had been isolated, when compared to businesses with non-related strains, thus substantiating the business-unit as an important epidemiological feature to consider in contact network analysis and tracing of infection routes. However, this approach may overestimate real-life contacts between cattle properties and putatively overestimate the degree of risk-contacts within each business, since it is based solely on information about property ownership and location. This does not consider administrative and individual farmers behaviours that essentially keep two properties separated. Despite this, we conclude that defining epidemiological units based on businesses is a promising approach for future disease tracing tasks.

Bayesian estimation of herd-level prevalence and risk factors associated with BoHV-1 infection in cattle herds in the State of Paraíba, Brazil.

A cross-sectional study was carried out to estimate the animal- and herd-level prevalence of bovine herpesvirus 1 (BoHV-1) infection in cattle in the State of Paraíba, and to identify risk factors associated with herd-level infection. The state was divided into three sampling strata, and for each stratum, the prevalence of herds infected with BoHV-1 was estimated through a two-stage sampling survey carried out from September 2012 to January 2013. In total, 2443 animals were sampled from 478 herds. A virus-neutralization test was used for BoHV-1 antibody detection. A Bayesian latent-class model was used to describe the data, taking into account imperfect diagnostic test characteristics and the non-independence of test results from animals within the same herd, and using a dynamic within-model risk factor selection method based on indicator variable selection. The adjusted herd-level prevalence was estimated to be 84% (95% CI: 80-88%) for the State of Paraíba, and the animal-level prevalence was estimated to be 73% (95% CI: 66-84%). Only five of the available risk factors were used by the model, with the three most influential being disposal of aborted foetuses (3.78, 95% CI: 1.11-13.85), sharing resources with other farms (3.0, 95% CI: 1.1-8,6), and a herd size of > 23 animals (2.5, 95% CI: 1.1-6.0). Our findings suggest that the animal- and herd-level seroprevalence of BoHV-1 infection in the State of Paraíba is high. While some risk factors such as herd size and sharing resources were identified as risk factors for BoHV-1 infection, these risk factors are initially likely to be of only minor relevance in a control programme due to the extremely high prevalence of infected farms. However, the results are relevant to the risk of reintroduction of disease on farms that have previously eradicated the disease.

Selection of Meat Inspection Data for an Animal Welfare Index in Cattle and Pigs in Denmark.

National welfare indices of cattle and pigs are constructed in Denmark, and meat inspection data may be used to contribute to these. We select potentially welfare-relevant abattoir recordings and assess the sources of variation within these with a view towards inclusion in the indices. Meat inspection codes were pre-selected based on expert judgement of having potential animal welfare relevance. Random effects logistic regression was then used to determine the magnitude of variation derived at the level of the farm or abattoir, of which farm variation might be associated with welfare, whereas abattoir variation is most likely caused by differences in recording practices. Codes were excluded for use in the indices based on poor model fit or a large abattoir effect. There was a large abattoir effect for most of the codes modelled and these codes were deemed to be not appropriate to be carried forward to the welfare index. A few were found to be potentially useful for a welfare index: Eight for slaughter pigs, 15 for sows, five for cattle <18 months of age, and six for older cattle. The absolute accuracy of each code/combination could not be assessed, only the relative variation between farms and abattoirs.