Literature DB >> 25843374

Thirteen challenges in modelling plant diseases.

Nik J Cunniffe1, Britt Koskella2, C Jessica E Metcalf3, Stephen Parnell4, Tim R Gottwald5, Christopher A Gilligan6.   

Abstract

The underlying structure of epidemiological models, and the questions that models can be used to address, do not necessarily depend on the host organism in question. This means that certain preoccupations of plant disease modellers are similar to those of modellers of diseases in human, livestock and wild animal populations. However, a number of aspects of plant epidemiology are very distinctive, and this leads to specific challenges in modelling plant diseases, which in turn sets a certain agenda for modellers. Here we outline a selection of 13 challenges, specific to plant disease epidemiology, that we feel are important targets for future work.
Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Compartmental models; Landscape-scale models; Plant disease model; Plant epidemiology; Stochastic models

Mesh:

Year:  2014        PMID: 25843374     DOI: 10.1016/j.epidem.2014.06.002

Source DB:  PubMed          Journal:  Epidemics        ISSN: 1878-0067            Impact factor:   4.396


  32 in total

1.  Early detection surveillance for an emerging plant pathogen: a rule of thumb to predict prevalence at first discovery.

Authors:  S Parnell; T R Gottwald; N J Cunniffe; V Alonso Chavez; F van den Bosch
Journal:  Proc Biol Sci       Date:  2015-09-07       Impact factor: 5.349

2.  Applying optimal control theory to complex epidemiological models to inform real-world disease management.

Authors:  E H Bussell; C E Dangerfield; C A Gilligan; N J Cunniffe
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2019-07-08       Impact factor: 6.237

3.  Preface to theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.

Authors:  R N Thompson; Ellen Brooks-Pollock
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2019-07-08       Impact factor: 6.237

4.  Trade-off between disease resistance and crop yield: a landscape-scale mathematical modelling perspective.

Authors:  Martin Vyska; Nik Cunniffe; Christopher Gilligan
Journal:  J R Soc Interface       Date:  2016-10       Impact factor: 4.118

5.  Evidence-based controls for epidemics using spatio-temporal stochastic models in a Bayesian framework.

Authors:  Hola K Adrakey; George Streftaris; Nik J Cunniffe; Tim R Gottwald; Christopher A Gilligan; Gavin J Gibson
Journal:  J R Soc Interface       Date:  2017-11       Impact factor: 4.118

6.  Assessing the multi-pathway threat from an invasive agricultural pest: Tuta absoluta in Asia.

Authors:  Joseph McNitt; Young Yun Chungbaek; Henning Mortveit; Madhav Marathe; Mateus R Campos; Nicolas Desneux; Thierry Brévault; Rangaswamy Muniappan; Abhijin Adiga
Journal:  Proc Biol Sci       Date:  2019-10-16       Impact factor: 5.349

7.  Adequacy of SEIR models when epidemics have spatial structure: Ebola in Sierra Leone.

Authors:  Wayne M Getz; Richard Salter; Whitney Mgbara
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2019-06-24       Impact factor: 6.237

8.  Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants.

Authors:  Robin N Thompson; Ellen Brooks-Pollock
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2019-06-24       Impact factor: 6.237

9.  Modeling when, where, and how to manage a forest epidemic, motivated by sudden oak death in California.

Authors:  Nik J Cunniffe; Richard C Cobb; Ross K Meentemeyer; David M Rizzo; Christopher A Gilligan
Journal:  Proc Natl Acad Sci U S A       Date:  2016-05-02       Impact factor: 11.205

10.  Applying optimal control theory to a spatial simulation model of sudden oak death: ongoing surveillance protects tanoak while conserving biodiversity.

Authors:  E H Bussell; N J Cunniffe
Journal:  J R Soc Interface       Date:  2020-04-01       Impact factor: 4.118

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