Thirteen challenges in modelling plant diseases

Nik J. Cunniffe; Britt Koskella; C. Jessica E. Metcalf; Stephen Parnell; Tim R. Gottwald; Christopher A. Gilligan

doi:10.1016/j.epidem.2014.06.002

Thirteen challenges in modelling plant diseases

Nik J. Cunniffe, Britt Koskella, C. Jessica E. Metcalf, Stephen Parnell, Tim R. Gottwald, Christopher A. Gilligan

Research output: Contribution to journal › Article › peer-review

109 Scopus citations

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.

Original language	English (US)
Pages (from-to)	6-10
Number of pages	5
Journal	Epidemics
Volume	10
DOIs	https://doi.org/10.1016/j.epidem.2014.06.002
State	Published - Mar 1 2015
Externally published	Yes

All Science Journal Classification (ASJC) codes

Public Health, Environmental and Occupational Health
Infectious Diseases
Epidemiology
Virology
Parasitology
Microbiology

Keywords

Compartmental models
Landscape-scale models
Plant disease model
Plant epidemiology
Stochastic models

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10.1016/j.epidem.2014.06.002

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title = "Thirteen challenges in modelling plant diseases",

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.",

keywords = "Compartmental models, Landscape-scale models, Plant disease model, Plant epidemiology, Stochastic models",

author = "Cunniffe, {Nik J.} and Britt Koskella and {E. Metcalf}, {C. Jessica} and Stephen Parnell and Gottwald, {Tim R.} and Gilligan, {Christopher A.}",

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AU - Cunniffe, Nik J.

AU - Koskella, Britt

AU - E. Metcalf, C. Jessica

AU - Parnell, Stephen

AU - Gottwald, Tim R.

AU - Gilligan, Christopher A.

PY - 2015/3/1

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AB - 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.

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KW - Plant epidemiology

KW - Stochastic models

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Thirteen challenges in modelling plant diseases

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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