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

View ORCID ProfileAurélien Madouasse, Mathilde Mercat, Annika van Roon, David Graham, Maria Guelbenzu, Inge Santman Berends, View ORCID ProfileGerdien van Schaik, Mirjam Nielen, Jenny Frössling, Estelle Ågren, Roger Humphry, View ORCID ProfileJude Eze, George Gunn, Madeleine Henry, Jörn Gethmann, Simon J. More, View ORCID ProfileChristine Fourichon
doi: https://doi.org/10.1101/2020.07.10.197426
Aurélien Madouasse
1BIOEPAR, INRA, Oniris, La Chantrerie, Nantes 44307, France
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  • For correspondence: aurelien.madouasse@oniris-nantes.fr
Mathilde Mercat
1BIOEPAR, INRA, Oniris, La Chantrerie, Nantes 44307, France
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Annika van Roon
2Department of Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, PO Box 80151, 3508, TD Utrecht, the Netherlands
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David Graham
3Animal Health Ireland, Unit 4/5, The Archways, Bridge St., Carrick-on-Shannon, Co. Leitrim N41 WN27, Ireland
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Maria Guelbenzu
3Animal Health Ireland, Unit 4/5, The Archways, Bridge St., Carrick-on-Shannon, Co. Leitrim N41 WN27, Ireland
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Inge Santman Berends
2Department of Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, PO Box 80151, 3508, TD Utrecht, the Netherlands
4GD Animal Health, PO Box 9, 7400 AA, Deventer, the Netherlands
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Gerdien van Schaik
2Department of Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, PO Box 80151, 3508, TD Utrecht, the Netherlands
4GD Animal Health, PO Box 9, 7400 AA, Deventer, the Netherlands
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Mirjam Nielen
2Department of Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, PO Box 80151, 3508, TD Utrecht, the Netherlands
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Jenny Frössling
5Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), 751 89 Uppsala, Sweden
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Estelle Ågren
5Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), 751 89 Uppsala, Sweden
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Roger Humphry
6Scotland’s Rural College, Kings Buildings, West Mains Road, Edinburgh, EH9 3JG, United Kingdom
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Jude Eze
6Scotland’s Rural College, Kings Buildings, West Mains Road, Edinburgh, EH9 3JG, United Kingdom
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George Gunn
6Scotland’s Rural College, Kings Buildings, West Mains Road, Edinburgh, EH9 3JG, United Kingdom
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Madeleine Henry
6Scotland’s Rural College, Kings Buildings, West Mains Road, Edinburgh, EH9 3JG, United Kingdom
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Jörn Gethmann
7Institute of Epidemiology, Friedrich-Loeffler-Institute, Südufer 10, 17493 Greifswald, Germany
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Simon J. More
8Centre for Veterinary Epidemiology and Risk Analysis, UCD School of Veterinary Medicine, University College Dublin, Belfield, Dublin D04 W6F6, Ireland
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Christine Fourichon
1BIOEPAR, INRA, Oniris, La Chantrerie, Nantes 44307, France
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  • ORCID record for Christine Fourichon
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Abstract

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 package (https://github.com/AurMad/STOCfree).

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Minor modifications to the discussion. List of references updated.

  • https://github.com/AurMad/STOCfree

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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A modelling framework for the prediction of the herd-level probability of infection from longitudinal data
Aurélien Madouasse, Mathilde Mercat, Annika van Roon, David Graham, Maria Guelbenzu, Inge Santman Berends, Gerdien van Schaik, Mirjam Nielen, Jenny Frössling, Estelle Ågren, Roger Humphry, Jude Eze, George Gunn, Madeleine Henry, Jörn Gethmann, Simon J. More, Christine Fourichon
bioRxiv 2020.07.10.197426; doi: https://doi.org/10.1101/2020.07.10.197426
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A modelling framework for the prediction of the herd-level probability of infection from longitudinal data
Aurélien Madouasse, Mathilde Mercat, Annika van Roon, David Graham, Maria Guelbenzu, Inge Santman Berends, Gerdien van Schaik, Mirjam Nielen, Jenny Frössling, Estelle Ågren, Roger Humphry, Jude Eze, George Gunn, Madeleine Henry, Jörn Gethmann, Simon J. More, Christine Fourichon
bioRxiv 2020.07.10.197426; doi: https://doi.org/10.1101/2020.07.10.197426

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