Agent-based model for Johne’s disease dynamics in a dairy herd

Agent-based model for Johne’s disease dynamics in a dairy herd

Johne’s disease is an infectious gastrointestinal disease in ruminants caused by Mycobacterium avium subsp. paratuberculosis that causes diarrhea, emaciation, decreased milk production and eventually death. The disease is transmitted in utero and via milk and colostrums to calves, and fecal-orally t...

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Main Authors: Robins, Jessica, Bogen, Sarah, Francis, Auldon, Westhoek, Annet, Kanarek, Andrew, Lenhart, Suzanne, Eda, Shigetoshi
Format: Online
Language:English
Published: BioMed Central 2015
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4474466/
id pubmed-4474466
recordtype oai_dc
spelling pubmed-44744662015-06-20 Agent-based model for Johne’s disease dynamics in a dairy herd Robins, Jessica Bogen, Sarah Francis, Auldon Westhoek, Annet Kanarek, Andrew Lenhart, Suzanne Eda, Shigetoshi Research Article Johne’s disease is an infectious gastrointestinal disease in ruminants caused by Mycobacterium avium subsp. paratuberculosis that causes diarrhea, emaciation, decreased milk production and eventually death. The disease is transmitted in utero and via milk and colostrums to calves, and fecal-orally to all age classes. Financial losses due to the disease are estimated to be over $200 million in the US dairy industry. The goal of this study was to evaluate the cost effectiveness of control measures based on diagnosis with a sensitive ELISA, EVELISA. An agent-based, discrete time model was developed to simulate Johne’s disease dynamics in a US dairy herd. Spatial aspects of disease transmission were taken into account by using six spatial compartments. The effects on disease prevalence were studied with and without transmission routes included in the model. Further, using the model, cost effectiveness of ELISA-based Johne’s disease control was evaluated. Using the parameters we collected and assumed, our model showed the initial prevalence of Johne’s disease (33.1 ± 0.2%) in the farm increased to 87.7 ± 1.7% in a 10 year-simulation. When ELISA-based control measures were included in the simulation, the increase in prevalence was significantly slowed down, especially when EVELISA was used. However, the level of the prevalence was still higher than the initial level after 10 year simulation even with the ELISA-based diagnostic intervention. The prevalence was further reduced when quarterly ELISA testing was included. The cost analysis showed that the quarterly ELISA and EVELISA testing could bring $44.8 and $51.5/animal/year more revenues, respectively, to a dairy farm. BioMed Central 2015-06-19 2015 /pmc/articles/PMC4474466/ /pubmed/26091904 http://dx.doi.org/10.1186/s13567-015-0195-y Text en © Robins et al. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Robins, Jessica
Bogen, Sarah
Francis, Auldon
Westhoek, Annet
Kanarek, Andrew
Lenhart, Suzanne
Eda, Shigetoshi
spellingShingle Robins, Jessica
Bogen, Sarah
Francis, Auldon
Westhoek, Annet
Kanarek, Andrew
Lenhart, Suzanne
Eda, Shigetoshi
Agent-based model for Johne’s disease dynamics in a dairy herd
author_facet Robins, Jessica
Bogen, Sarah
Francis, Auldon
Westhoek, Annet
Kanarek, Andrew
Lenhart, Suzanne
Eda, Shigetoshi
author_sort Robins, Jessica
title Agent-based model for Johne’s disease dynamics in a dairy herd
title_short Agent-based model for Johne’s disease dynamics in a dairy herd
title_full Agent-based model for Johne’s disease dynamics in a dairy herd
title_fullStr Agent-based model for Johne’s disease dynamics in a dairy herd
title_full_unstemmed Agent-based model for Johne’s disease dynamics in a dairy herd
title_sort agent-based model for johne’s disease dynamics in a dairy herd
description Johne’s disease is an infectious gastrointestinal disease in ruminants caused by Mycobacterium avium subsp. paratuberculosis that causes diarrhea, emaciation, decreased milk production and eventually death. The disease is transmitted in utero and via milk and colostrums to calves, and fecal-orally to all age classes. Financial losses due to the disease are estimated to be over $200 million in the US dairy industry. The goal of this study was to evaluate the cost effectiveness of control measures based on diagnosis with a sensitive ELISA, EVELISA. An agent-based, discrete time model was developed to simulate Johne’s disease dynamics in a US dairy herd. Spatial aspects of disease transmission were taken into account by using six spatial compartments. The effects on disease prevalence were studied with and without transmission routes included in the model. Further, using the model, cost effectiveness of ELISA-based Johne’s disease control was evaluated. Using the parameters we collected and assumed, our model showed the initial prevalence of Johne’s disease (33.1 ± 0.2%) in the farm increased to 87.7 ± 1.7% in a 10 year-simulation. When ELISA-based control measures were included in the simulation, the increase in prevalence was significantly slowed down, especially when EVELISA was used. However, the level of the prevalence was still higher than the initial level after 10 year simulation even with the ELISA-based diagnostic intervention. The prevalence was further reduced when quarterly ELISA testing was included. The cost analysis showed that the quarterly ELISA and EVELISA testing could bring $44.8 and $51.5/animal/year more revenues, respectively, to a dairy farm.
publisher BioMed Central
publishDate 2015
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4474466/
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