Evidence map›Paper›PMID 31104598›Full record

ReviewPhilosophical transactions of the Royal Society of London. Series B, Biological sciences2019

Forecasting and control of emerging infectious forest disease through participatory modelling.

Devon A Gaydos, Anna Petrasova, Richard C Cobb, Ross K Meentemeyer

Open access · hybridAbstract readReview
In one paragraph

Review in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
4.1field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 27 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Evaluating online and tangible interfaces for engaging stakeholders in forecasting and control of biological invasions.Ecological applications : a publication of the Ecological Society of America · 2021
    Article
  5. Article
  6. Review
  7. Article
  8. Preface to theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019
    Article
  9. Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Devon A Gaydos1 Department of Forestry and Environmental Resources, North Carolina State University , 2800 Faucette Drive, Raleigh, NC 27606 , USA.
Anna Petrasova2 Center for Geospatial Analytics, North Carolina State University , 2800 Faucette Drive, Raleigh, NC 27606 , USA.
Richard C Cobb3 Department of Natural Resources and Environmental Science, California Polytechnic State University , San Luis Obispo, CA 93407 , USA.
Ross K Meentemeyer1 Department of Forestry and Environmental Resources, North Carolina State University , 2800 Faucette Drive, Raleigh, NC 27606 , USA.
North Carolina State University · USCalifornia Polytechnic State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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'.

Indexed as

Communicable Diseases, EmergingForecastingModels, BiologicalPlant Diseasesforest diseasegeospatiallandscape epidemiologyparticipatory researchstakeholder engagementtangible interaction

Identifiers

PMID31104598
PMCPMC6558554
OpenAlexW2945365129

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.