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    Author(s): Devon A. Gaydos; Anna Petrasova; Richard C. Cobb; Ross K. Meentemeyer
    Date: 2019
    Source: Philosophical Transactions of the Royal Society B: Biological Sciences
    Publication Series: Scientific Journal (JRNL)
    Station: Pacific Southwest Research Station
    PDF: Download Publication  (676.0 KB)


    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.

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    Gaydos, Devon A.; Petrasova, Anna; Cobb, Richard C.; Meentemeyer, Ross K. 2019. Forecasting and control of emerging infectious forest disease through participatory modelling. Philosophical Transactions of the Royal Society B: Biological Sciences. 374(1776): 20180283.


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    participatory research, stakeholder engagement, landscape epidemiology, forest disease, geospatial, tangible interaction

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