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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 W. Humphry, View ORCID ProfileJude Eze, George J. Gunn, Madeleine K. Henry, Jörn Gethmann, Simon J. More, View ORCID ProfileNils Toft, View ORCID ProfileChristine Fourichon
doi: https://doi.org/10.1101/2020.07.10.197426
Aurélien Madouasse
1BIOEPAR, INRAE, Oniris – Nantes, France
Mathilde Mercat
1BIOEPAR, INRAE, Oniris – Nantes, France
Annika van Roon
2Department of Population Health Sciences, Unit Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University – Utrecht, the Netherlands
David Graham
3Animal Health Ireland, Unit 4/5 – Carrick-on-Shannon, Ireland
Maria Guelbenzu
3Animal Health Ireland, Unit 4/5 – Carrick-on-Shannon, Ireland
Inge Santman Berends
2Department of Population Health Sciences, Unit Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University – Utrecht, the Netherlands
4Royal GD – Deventer, the Netherlands
Gerdien van Schaik
2Department of Population Health Sciences, Unit Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University – Utrecht, the Netherlands
4Royal GD – Deventer, the Netherlands
Mirjam Nielen
2Department of Population Health Sciences, Unit Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University – Utrecht, the Netherlands
Jenny Frössling
5Department of Disease Control and Epidemiology, National Veterinary Institute (SVA) – Uppsala, Sweden
6Department of Animal Environment and Health, Swedish University of Agricultural Sciences – Skara, Sweden
Estelle Ågren
5Department of Disease Control and Epidemiology, National Veterinary Institute (SVA) – Uppsala, Sweden
6Department of Animal Environment and Health, Swedish University of Agricultural Sciences – Skara, Sweden
Roger W. Humphry
7Epidemiology Research Unit, Scotland’s Rural College – Edinburgh, United Kingdom
Jude Eze
7Epidemiology Research Unit, Scotland’s Rural College – Edinburgh, United Kingdom
George J. Gunn
7Epidemiology Research Unit, Scotland’s Rural College – Edinburgh, United Kingdom
Madeleine K. Henry
7Epidemiology Research Unit, Scotland’s Rural College – Edinburgh, United Kingdom
Jörn Gethmann
8Institute of Epidemiology, Friedrich-Loeffler-Institut – Federal Research Institute for Animal Health (FLI) – Riems, Germany
Simon J. More
9Centre for Veterinary Epidemiology and Risk Analysis, UCD School of Veterinary Medicine, University College Dublin – Dublin, Ireland
Nils Toft
10IQinAbox ApS – Værløse, Denmark
Christine Fourichon
1BIOEPAR, INRAE, Oniris – Nantes, France
Posted September 01, 2021.
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 W. Humphry, Jude Eze, George J. Gunn, Madeleine K. Henry, Jörn Gethmann, Simon J. More, Nils Toft, Christine Fourichon
bioRxiv 2020.07.10.197426; doi: https://doi.org/10.1101/2020.07.10.197426
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 W. Humphry, Jude Eze, George J. Gunn, Madeleine K. Henry, Jörn Gethmann, Simon J. More, Nils Toft, Christine Fourichon
bioRxiv 2020.07.10.197426; doi: https://doi.org/10.1101/2020.07.10.197426
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