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  • 1.
    Dórea, Fernanda C.
    et al.
    Department of Disease Control and Epidemiology, National Veterinary Institute, Sweden.
    Vial, Flavie
    Epi-Connect, Skogås, Sweden.
    Hammar, Karl
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering. Department of Computer Science and Informatics, Jönköping University, Sweden.
    Lindberg, Ann
    Department of Disease Control and Epidemiology, National Veterinary Institute, Sweden.
    Lambrix, Patrick
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
    Blomqvist, Eva
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
    Revie, Crawford W.
    Atlantic Veterinary College, University of Prince Edward Island, Canada.
    Drivers for the development of an Animal Health Surveillance Ontology (AHSO)2019In: Preventive Veterinary Medicine, ISSN 0167-5877, E-ISSN 1873-1716, Vol. 166, no 1, p. 39-48Article in journal (Refereed)
    Abstract [en]

    Comprehensive reviews of syndromic surveillance in animal health have highlighted the hindrances to integration and interoperability among systems when data emerge from different sources. Discussions with syndromic surveillance experts in the fields of animal and public health, as well as computer scientists from the field of information management, have led to the conclusion that a major component of any solution will involve the adoption of ontologies. Here we describe the advantages of such an approach, and the steps taken to set up the Animal Health Surveillance Ontological (AHSO) framework. The AHSO framework is modelled in OWL, the W3C standard Semantic Web language for representing rich and complex knowledge. We illustrate how the framework can incorporate knowledge directly from domain experts or from data-driven sources, as well as by integrating existing mature ontological components from related disciplines. The development and extent of AHSO will be community driven and the final products in the framework will be open-access.

  • 2.
    Frossling, Jenny
    et al.
    SVA, Sweden Swedish University of Agriculture Science, Sweden .
    Ohlson, Anna
    SVA, Sweden Swedish University of Agriculture Science, Sweden .
    Bjorkman, Camilla
    Swedish University of Agriculture Science, Sweden .
    Håkansson, Nina
    Linköping University, Department of Physics, Chemistry and Biology, Theoretical Biology. Linköping University, The Institute of Technology.
    Noremark, Maria
    SVA, Sweden .
    Application of network analysis parameters in risk-based surveillance - Examples based on cattle trade data and bovine infections in Sweden2012In: Preventive Veterinary Medicine, ISSN 0167-5877, E-ISSN 1873-1716, Vol. 105, no 3, p. 202-208Article in journal (Refereed)
    Abstract [en]

    Financial resources may limit the number of samples that can be collected and analysed in disease surveillance programmes. When the aim of surveillance is disease detection and identification of case herds, a risk-based approach can increase the sensitivity of the surveillance system. In this paper, the association between two network analysis measures, i.e. in-degree and ingoing infection chain, and signs of infection is investigated. It is shown that based on regression analysis of combined data from a recent cross-sectional study for endemic viral infections and network analysis of animal movements, a positive serological result for bovine coronavirus (BCV) and bovine respiratory syncytial virus (BRSV) is significantly associated with the purchase of animals. For BCV, this association was significant also when accounting for herd size and regional cattle density, but not for BRSV. Examples are given for different approaches to include cattle movement data in risk-based surveillance by selecting herds based on network analysis measures. Results show that compared to completely random sampling these approaches increase the number of detected positives, both for BCV and BRSV in our study population. It is concluded that network measures for the relevant time period based on updated databases of animal movements can provide a simple and straight forward tool for risk-based sampling.

  • 3.
    Gorsich, Erin E.
    et al.
    Colorado State Univ, CO 80523 USA.
    McKee, Clifton D.
    Colorado State Univ, CO 80523 USA.
    Grear, Daniel A.
    USDA APHIS Vet Serv, CO USA.
    Miller, Ryan S.
    USDA APHIS Vet Serv, CO USA.
    Portacci, Katie
    USDA APHIS Vet Serv, CO USA.
    Lindström, Tom
    Linköping University, Department of Physics, Chemistry and Biology, Theoretical Biology. Linköping University, Faculty of Science & Engineering.
    Webb, Colleen T.
    Colorado State Univ, CO 80523 USA; Colorado State Univ, CO 80523 USA.
    Model-guided suggestions for targeted surveillance based on cattle shipments in the US2018In: Preventive Veterinary Medicine, ISSN 0167-5877, E-ISSN 1873-1716, Vol. 150, p. 52-59Article in journal (Refereed)
    Abstract [en]

    Risk-based sampling is an essential component of livestock health surveillance because it targets resources towards sub-populations with a higher risk of infection. Risk-based surveillance in U.S. livestock is limited because the locations of high-risk herds are often unknown and data to identify high-risk herds based on shipments are often unavailable. In this study, we use a novel, data-driven network model for the shipments of cattle in the U.S. (the U.S. Animal Movement Model, USAMM) to provide surveillance suggestions for cattle imported into the U.S. from Mexico. We describe the volume and locations where cattle are imported and analyze their predicted shipment patterns to identify counties that are most likely to receive shipments of imported cattle. Our results suggest that most imported cattle are sent to relatively few counties. Surveillance at 10 counties is predicted to sample 22-34% of imported cattle while surveillance at 50 counties is predicted to sample 43%-61% of imported cattle. These findings are based on the assumption that USAMM accurately describes the shipments of imported cattle because their shipments are not tracked separately from the remainder of the U.S. herd. However, we analyze two additional datasets - Interstate Certificates of Veterinary Inspection and brand inspection data - to ensure that the characteristics of potential post-import shipments do not change on an annual scale and are not dependent on the dataset informing our analyses. Overall, these results highlight the utility of USAMM to inform targeted surveillance strategies when complete shipment information is unavailable.

  • 4.
    Lindström, Tom
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Theoretical Biology . Linköping University, The Institute of Technology.
    Sisson, Scott A.
    School of Mathematics and Statistics, University of New South Wales, Sydney 2052, Australia.
    Nöremark, Maria
    Department of Disease Control and Epidemiology, SVA, National Veterinary Institute, 751 89 Uppsala, Sweden.
    Jonsson, Annie
    Research Centre of Systems Biology, Ecological Modelling, University of Skövde, 541 28 Skövde, Sweden.
    Wennergren, Uno
    Linköping University, Department of Physics, Chemistry and Biology, Theoretical Biology . Linköping University, The Institute of Technology.
    Estimation of distance related probability of animal movements between holdings and implications for disease spread modeling2009In: Preventive Veterinary Medicine, ISSN 0167-5877, E-ISSN 1873-1716, Vol. 91, no 2-4, p. 85-94Article in journal (Refereed)
    Abstract [en]

    Between holding contacts are more common over short distances and this may have implications for the dynamics of disease spread through these contacts. A reliable estimation of how contacts depend on distance is therefore important when modeling livestock diseases. In this study, we have developed a method for analyzing distant dependent contacts and applied it to animal movement data from Sweden. The data were analyzed with two competing models. The first model assumes that contacts arise from a purely distance dependent process. The second is a mixture model and assumes that, in addition, some contacts arise independent of distance. Parameters were estimated with a Bayesian Markov Chain Monte Carlo (MCMC) approach and the model probabilities were compared. We also investigated possible between model differences in predicted contact structures, using a collection of network measures. We found that the mixture model was a much better model for the data analyzed. Also, the network measures showed that the models differed considerably in predictions of contact structures, which is expected to be important for disease spread dynamics. We conclude that a model with contacts being both dependent on, and independent of, distance was preferred for modeling the example animal movement contact data.

  • 5.
    Lindström, Tom
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Theoretical Biology . Linköping University, The Institute of Technology.
    Sisson, Scott A.
    School of Mathematics and Statistics, University of New South Wales, Sydney 2052, Australia.
    Stenberg Lewerin, Susanna
    Department of Disease Control and Epidemiology, SVA, National Veterinary Institute, 751 89 Uppsala, Sweden.
    Wennergren, Uno
    Linköping University, Department of Physics, Chemistry and Biology, Theoretical Biology . Linköping University, The Institute of Technology.
    Bayesian analysis of animal movements related to factors at herdand between herd levels: Implications for disease spread modeling2011In: Preventive Veterinary Medicine, ISSN 0167-5877, E-ISSN 1873-1716, Vol. 98, no 4, p. 230-242Article in journal (Other academic)
    Abstract [en]

    A method to assess the influence of between herd distances, production types and herd sizes on patterns of between herd contacts is presented. It was applied on pig movement data from a central database of Swedish Board of Agriculture. To determine the influence of these factors on the contact between holdings we used a Bayesian model and Markov chain Monte Carlo (MCMC) methods to estimate the posterior distribution of model parameters. The analysis showed that the contact pattern via animal movements is highly heterogeneous and influenced by all three factors, production type, herd size, and distance between farms. Most production types showed a positive relationship between maximum capacity and the probability of both incoming and outgoing movements. In agreement with previous studies, holdings also differed in both the number of contacts as well as with what holding types contact occurred with. Also, the scale and shape of distance dependence in contact probability was shown to differ depending on the production types of holdings.

    To demonstrate how the methodology may be used for risk assessment, disease transmissions via animal movements were simulated with the model used for analysis of contacts, and parameterized by the analyzed posterior distribution. A Generalized Linear Model showed that herds with production types Sow pool center, Multiplying herd and Nucleus herd have higher risk of generating a large number of new infections. Multiplying herds are also expected to generate many long distance transmissions, while transmissions generated by Sow pool centers are confined to more local areas. We argue that the methodology presented may be a useful tool for improvement of risk assessment based on data found in central databases.

  • 6.
    Lindström, Tom
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Theoretical Biology . Linköping University, The Institute of Technology.
    Sisson, Scott A
    School of Mathematics and Statistics, University of New South Wales, Sydney 2052, Australia.
    Stenberg Lewerin, Susanna
    Department of Disease Control and Epidemiology, SVA, National Veterinary Institute, 751 89 Uppsala, Sweden.
    Wennergren, Uno
    Linköping University, Department of Physics, Chemistry and Biology, Theoretical Biology . Linköping University, The Institute of Technology.
    Estimating animal movement contacts between holdings of different production types2010In: Preventive Veterinary Medicine, ISSN 0167-5877, E-ISSN 1873-1716, Vol. 95, no 1-2, p. 23-31Article in journal (Refereed)
    Abstract [en]

    Animal movement poses a great risk for disease transmission between holdings. Heterogeneous contact patterns are known to influence the dynamics of disease transmission and should be included in modeling. Using pig movement data from Sweden as an example, we present a method for quantification of between holding contact probabilities based on different production types. The data contained seven production types: Sow pool center, Sow pool satellite, Farrow-to-finish, Nucleus herd, Piglet producer, Multiplying herd and Fattening herd. The method also estimates how much different production types will determine the contact pattern of holdings that have more than one type. The method is based on Bayesian analysis and uses data from central databases of animal movement. Holdings with different production types are estimated to vary in the frequency of contacts as well as in what type of holding they have contact with, and the direction of the contacts. Movements from Multiplying herds to Sow pool centers, Nucleus herds to other Nucleus herds, Sow pool centers to Sow pool satellites, Sow pool satellites to Sow pool centers and Nucleus herds to Multiplying herds were estimated to be most common relative to the abundance of the production types. We show with a simulation study that these contact patterns may also be expected to result in substantial differences in disease transmission via animal movements, depending on the index holding. Simulating transmission for a 1 year period showed that the median number of infected holdings was 1 (i.e. only the index holding infected) if the infection started at a Fattening herd and 2161 if the infection started on a Nucleus herd. We conclude that it is valuable to include production types in models of disease transmission and the method presented in this paper may be used for such models when appropriate data is available. We also argue that keeping records of production types is of great value since it may be helpful in risk assessments.

  • 7.
    Svensson, Catarina
    et al.
    Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Alvåsen, Karin
    Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Eldh, Ann Catrine
    Division of Nursing, Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden.
    Frössling, Jenny
    Department of Disease Control and Epidemiology, National Veterinary Institute, Uppsala, Sweden; Department of Animal Environment and Health, Swedish University of Agricultural Sciences, Skara, Sweden.
    Lomander, H.
    District Veterinary Organization, Swedish Board of Agriculture, Tibro, Sweden.
    Veterinary herd health management: Experience among farmers and farm managers in Swedish dairy production2018In: Preventive Veterinary Medicine, ISSN 0167-5877, E-ISSN 1873-1716, Vol. 155, p. 45-52Article in journal (Refereed)
    Abstract [en]

    A preventive herd health approach will most likely reduce incidences of clinical and subclinical disease. Swedish veterinary organizations offer specific veterinary herd health management (HHM) programs, but these services are not used to a large extent.

    The aim of this study was to investigate dairy farmers’ experience of HHM and the conditions for collaboration with veterinarians in HHM.

    Six focus group discussions were conducted in March 2015 in West Sweden. In total, 33 dairy farmers participated. The recordings were transcribed and coded using thematic analysis, and the transcripts were reviewed to identify potential factors indicating barriers for farmers to engage a veterinarian in HHM. The participants reported HHM to be important, but they had difficulty defining the actions included in the concept. They described a wide range of their work duties as preventive. The farmers’ list of potential contributions by the veterinarians in HHM was strikingly short compared to the considerable number of preventive measures they performed themselves. Four main obstacles for farmers and farm managers to engage a veterinarian in HHM on their farm were identified in the analysis: “costs”, “veterinary knowledge, skills, and organization”, “farmer attitudes”, and “veterinarian-farmer relationships”. Costs were proposed as the main reason against engaging a veterinarian in HHM and included a high veterinary bill, low cost-benefit of veterinary services, and high costs to implement advice. Poor veterinary competence in HHM and poor knowledge about effective measures, practical farming, and farm economics were other important obstacles. Veterinarians were perceived to insufficiently describe their services and their benefits, and several participants felt they had never been offered veterinary HHM. Although veterinary HHM may be initiated by the farmer, the participants expected the veterinarian to have special responsibility for the initiation. A firm trust between farmer, staff, and veterinarian was considered crucial for veterinary HHM, but such trust takes a long time to build and can easily be disrupted by, for example, a veterinarian’s poor communication skills or lack of time.

    Our findings suggest that Swedish dairy farmers and herd managers find disease prevention important and that they perform a wide range of tasks to prevent disease in their animals. However, they do not see what role the veterinarian can play, and veterinarians were mainly associated with treating unhealthy cows. In order to increase the use of veterinary HHM programs the services and potential benefits of such programs need to be communicated more proactively.

  • 8.
    Webb, Colleen T.
    et al.
    Colorado State University, CO 80523 USA.
    Ferrari, Matthew
    Penn State University, PA 16802 USA.
    Lindström, Tom
    Linköping University, Department of Physics, Chemistry and Biology, Theoretical Biology. Linköping University, Faculty of Science & Engineering. Colorado State University, CO 80523 USA.
    Carpenter, Tim
    Massey University, New Zealand.
    Duerr, Salome
    University of Bern, Switzerland.
    Garner, Graeme
    Department Agriculture, Australia.
    Jewell, Chris
    Massey University, Palmerston North, New Zealand.
    Stevenson, Mark
    University of Melbourne, Australia.
    Ward, Michael P.
    University of Sydney, Australia.
    Werkman, Marleen
    CVI, Netherlands.
    Backer, Jantien
    CVI, Netherlands.
    Tildesley, Michael
    University of Warwick, England.
    Ensemble modelling and structured decision-making to support Emergency Disease Management2017In: Preventive Veterinary Medicine, ISSN 0167-5877, E-ISSN 1873-1716, Vol. 138, p. 124-133Article in journal (Refereed)
    Abstract [en]

    Epidemiological models in animal health are commonly used as decision-support tools to understand the impact of various control actions on infection spread in susceptible populations. Different models contain different assumptions and parameterizations, and policy decisions might be improved by considering outputs from multiple models. However, a transparent decision-support framework to integrate outputs from multiple models is nascent in epidemiology. Ensemble modelling and structured decision-making integrate the outputs of multiple models, compare policy actions and support policy decision-making. We briefly review the epidemiological application of ensemble modelling and structured decision-making and illustrate the potential of these methods using foot and mouth disease (FMD) models. In case study one, we apply structured decision-making to compare five possible control actions across three FMD models and show which control actions and outbreak costs are robustly supported and which are impacted by model uncertainty. In case study two, we develop a methodology for weighting the outputs of different models and show how different weighting schemes may impact the choice of control action. Using these case studies, we broadly illustrate the potential of ensemble modelling and structured decision-making in epidemiology to provide better information for decision-making and outline necessary development of these methods for their further application. Crown Copyright (C) 2017 Published by Elsevier B.V. All rights reserved.

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