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Forecasting and control of emerging infectious forest disease through participatory modelling

Epidemiological models are powerful tools for evaluating scenarios and visualizing patterns of disease spread, especially when comparing intervention strategies. However, the technical skill required to synthesize and operate computational models frequently renders them beyond the command of the sta...

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Detalles Bibliográficos
Autores principales: Gaydos, Devon A., Petrasova, Anna, Cobb, Richard C., Meentemeyer, Ross K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6558554/
https://www.ncbi.nlm.nih.gov/pubmed/31104598
http://dx.doi.org/10.1098/rstb.2018.0283
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author Gaydos, Devon A.
Petrasova, Anna
Cobb, Richard C.
Meentemeyer, Ross K.
author_facet Gaydos, Devon A.
Petrasova, Anna
Cobb, Richard C.
Meentemeyer, Ross K.
author_sort Gaydos, Devon A.
collection PubMed
description Epidemiological models are powerful tools for evaluating scenarios and visualizing patterns of disease spread, especially when comparing intervention strategies. However, the technical skill required to synthesize and operate computational models frequently renders them beyond the command of the stakeholders who are most impacted by the results. Participatory modelling (PM) strives to restructure the power relationship between modellers and the stakeholders who rely on model insights by involving these stakeholders directly in model development and application; yet, a systematic literature review indicates little adoption of these techniques in epidemiology, especially plant epidemiology. We investigate the potential for PM to integrate stakeholder and researcher knowledge, using Phytophthora ramorum and the resulting sudden oak death disease as a case study. Recent introduction of a novel strain (European 1 or EU1) in southwestern Oregon has prompted significant concern and presents an opportunity for coordinated management to minimize regional pathogen impacts. Using a PM framework, we worked with local stakeholders to develop an interactive forecasting tool for evaluating landscape-scale control strategies. We find that model co-development has great potential to empower stakeholders in the design, development and application of epidemiological models for disease control. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.
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spelling pubmed-65585542019-06-26 Forecasting and control of emerging infectious forest disease through participatory modelling Gaydos, Devon A. Petrasova, Anna Cobb, Richard C. Meentemeyer, Ross K. Philos Trans R Soc Lond B Biol Sci Articles Epidemiological models are powerful tools for evaluating scenarios and visualizing patterns of disease spread, especially when comparing intervention strategies. However, the technical skill required to synthesize and operate computational models frequently renders them beyond the command of the stakeholders who are most impacted by the results. Participatory modelling (PM) strives to restructure the power relationship between modellers and the stakeholders who rely on model insights by involving these stakeholders directly in model development and application; yet, a systematic literature review indicates little adoption of these techniques in epidemiology, especially plant epidemiology. We investigate the potential for PM to integrate stakeholder and researcher knowledge, using Phytophthora ramorum and the resulting sudden oak death disease as a case study. Recent introduction of a novel strain (European 1 or EU1) in southwestern Oregon has prompted significant concern and presents an opportunity for coordinated management to minimize regional pathogen impacts. Using a PM framework, we worked with local stakeholders to develop an interactive forecasting tool for evaluating landscape-scale control strategies. We find that model co-development has great potential to empower stakeholders in the design, development and application of epidemiological models for disease control. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. The Royal Society 2019-07-08 2019-05-20 /pmc/articles/PMC6558554/ /pubmed/31104598 http://dx.doi.org/10.1098/rstb.2018.0283 Text en © 2019 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
spellingShingle Articles
Gaydos, Devon A.
Petrasova, Anna
Cobb, Richard C.
Meentemeyer, Ross K.
Forecasting and control of emerging infectious forest disease through participatory modelling
title Forecasting and control of emerging infectious forest disease through participatory modelling
title_full Forecasting and control of emerging infectious forest disease through participatory modelling
title_fullStr Forecasting and control of emerging infectious forest disease through participatory modelling
title_full_unstemmed Forecasting and control of emerging infectious forest disease through participatory modelling
title_short Forecasting and control of emerging infectious forest disease through participatory modelling
title_sort forecasting and control of emerging infectious forest disease through participatory modelling
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6558554/
https://www.ncbi.nlm.nih.gov/pubmed/31104598
http://dx.doi.org/10.1098/rstb.2018.0283
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