Cargando…

Development of a stochastic agent-based model to evaluate surveillance strategies for detection of emergent porcine reproductive and respiratory syndrome strains

BACKGROUND: The objective of the current study was to develop a stochastic agent-based model using empirical data from Ontario (Canada) swine sites in order to evaluate different surveillance strategies for detection of emerging porcine reproductive and respiratory syndrome virus (PRRSV) strains at...

Descripción completa

Detalles Bibliográficos
Autores principales: Arruda, A. G., Poljak, Z., Knowles, D., McLean, A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5468968/
https://www.ncbi.nlm.nih.gov/pubmed/28606148
http://dx.doi.org/10.1186/s12917-017-1091-7
_version_ 1783243493486886912
author Arruda, A. G.
Poljak, Z.
Knowles, D.
McLean, A.
author_facet Arruda, A. G.
Poljak, Z.
Knowles, D.
McLean, A.
author_sort Arruda, A. G.
collection PubMed
description BACKGROUND: The objective of the current study was to develop a stochastic agent-based model using empirical data from Ontario (Canada) swine sites in order to evaluate different surveillance strategies for detection of emerging porcine reproductive and respiratory syndrome virus (PRRSV) strains at the regional level. Four strategies were evaluated, including (i) random sampling of fixed numbers of swine sites monthly; (ii) risk-based sampling of fixed numbers, specifically of breeding sites (high-consequence sites); (iii) risk-based sampling of fixed numbers of low biosecurity sites (high-risk); and (iv) risk-based sampling of breeding sites that are characterized as low biosecurity sites (high-risk/high-consequence). The model simulated transmission of a hypothetical emerging PRRSV strain between swine sites through three important industry networks (production system, truck and feed networks) while considering sites’ underlying immunity due to past or recent exposure to heterologous PRRSV strains, as well as demographic, geographic and biosecurity-related PRRS risk factors. Outcomes of interest included surveillance system sensitivity and time to detection of the three first cases over a period of approximately three years. RESULTS: Surveillance system sensitivities were low and time to detection of three first cases was long across all examined scenarios. CONCLUSION: Traditional modes of implementing high-risk and high-consequence risk-based surveillance based on site’s static characteristics do not appear to substantially improve surveillance system sensitivity. Novel strategies need to be developed and considered for rapid detection of this and other emerging swine infectious diseases. None of the four strategies compared herein appeared optimal for early detection of an emerging PPRSV strain at the regional level considering model assumptions, the underlying population of interest, and absence of other forms of surveillance.
format Online
Article
Text
id pubmed-5468968
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-54689682017-06-14 Development of a stochastic agent-based model to evaluate surveillance strategies for detection of emergent porcine reproductive and respiratory syndrome strains Arruda, A. G. Poljak, Z. Knowles, D. McLean, A. BMC Vet Res Research Article BACKGROUND: The objective of the current study was to develop a stochastic agent-based model using empirical data from Ontario (Canada) swine sites in order to evaluate different surveillance strategies for detection of emerging porcine reproductive and respiratory syndrome virus (PRRSV) strains at the regional level. Four strategies were evaluated, including (i) random sampling of fixed numbers of swine sites monthly; (ii) risk-based sampling of fixed numbers, specifically of breeding sites (high-consequence sites); (iii) risk-based sampling of fixed numbers of low biosecurity sites (high-risk); and (iv) risk-based sampling of breeding sites that are characterized as low biosecurity sites (high-risk/high-consequence). The model simulated transmission of a hypothetical emerging PRRSV strain between swine sites through three important industry networks (production system, truck and feed networks) while considering sites’ underlying immunity due to past or recent exposure to heterologous PRRSV strains, as well as demographic, geographic and biosecurity-related PRRS risk factors. Outcomes of interest included surveillance system sensitivity and time to detection of the three first cases over a period of approximately three years. RESULTS: Surveillance system sensitivities were low and time to detection of three first cases was long across all examined scenarios. CONCLUSION: Traditional modes of implementing high-risk and high-consequence risk-based surveillance based on site’s static characteristics do not appear to substantially improve surveillance system sensitivity. Novel strategies need to be developed and considered for rapid detection of this and other emerging swine infectious diseases. None of the four strategies compared herein appeared optimal for early detection of an emerging PPRSV strain at the regional level considering model assumptions, the underlying population of interest, and absence of other forms of surveillance. BioMed Central 2017-06-12 /pmc/articles/PMC5468968/ /pubmed/28606148 http://dx.doi.org/10.1186/s12917-017-1091-7 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.
spellingShingle Research Article
Arruda, A. G.
Poljak, Z.
Knowles, D.
McLean, A.
Development of a stochastic agent-based model to evaluate surveillance strategies for detection of emergent porcine reproductive and respiratory syndrome strains
title Development of a stochastic agent-based model to evaluate surveillance strategies for detection of emergent porcine reproductive and respiratory syndrome strains
title_full Development of a stochastic agent-based model to evaluate surveillance strategies for detection of emergent porcine reproductive and respiratory syndrome strains
title_fullStr Development of a stochastic agent-based model to evaluate surveillance strategies for detection of emergent porcine reproductive and respiratory syndrome strains
title_full_unstemmed Development of a stochastic agent-based model to evaluate surveillance strategies for detection of emergent porcine reproductive and respiratory syndrome strains
title_short Development of a stochastic agent-based model to evaluate surveillance strategies for detection of emergent porcine reproductive and respiratory syndrome strains
title_sort development of a stochastic agent-based model to evaluate surveillance strategies for detection of emergent porcine reproductive and respiratory syndrome strains
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5468968/
https://www.ncbi.nlm.nih.gov/pubmed/28606148
http://dx.doi.org/10.1186/s12917-017-1091-7
work_keys_str_mv AT arrudaag developmentofastochasticagentbasedmodeltoevaluatesurveillancestrategiesfordetectionofemergentporcinereproductiveandrespiratorysyndromestrains
AT poljakz developmentofastochasticagentbasedmodeltoevaluatesurveillancestrategiesfordetectionofemergentporcinereproductiveandrespiratorysyndromestrains
AT knowlesd developmentofastochasticagentbasedmodeltoevaluatesurveillancestrategiesfordetectionofemergentporcinereproductiveandrespiratorysyndromestrains
AT mcleana developmentofastochasticagentbasedmodeltoevaluatesurveillancestrategiesfordetectionofemergentporcinereproductiveandrespiratorysyndromestrains