Location: Subtropical Plant Pathology Research
Title: Thirteen challenges in modelling plant diseases Authors
|Cunniffe, N -|
|Koskella, B -|
|Metcalf, J -|
|Parnell, S -|
|Gilligan, C -|
Submitted to: Elsevier
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: June 23, 2014
Publication Date: July 1, 2014
Citation: Cunniffe, N.J., Koskella, B., Metcalf, J.E., Parnell, S., Gottwald, T.R., Gilligan, C.A. 2014. Thirteen challenges in modelling plant diseases. Elsevier. 10:6-10 doi.org/10.1016/j.epidem.2014.06.002. Interpretive Summary: This is a review article requested by the journal of Epidemic for a special issue. The journal editors requested this article from a cadre of world’s experts on plant disease modeling. In this article we discuss the strengths and weaknesses of plant disease modeling, the kind of data to use and its proper interpretation. We also explore a number of specific topics that are very challenging to modelers of plant disease including: Linking epidemiological models to crop yield, how plant host populations change through time, how to capture the structure of the host population over the landscape, how to deal with alternate host for the disease, how to make models more realistic for disease spread and include the influence of weather, how to represent that infection of a disease varies over time, discuss how of vectors, especially insects, transmit the disease, how we can use models to optimize disease detection, how we can account for the economics of disease control options, and finally how to make models useable by policy makers and stakeholders.
Technical Abstract: The underlying structure of epidemiological models, and the questions that models can be used to address, do not necessarily depend on the identity of the host. This means that certain preoccupations of plant disease modelers are similar to those of modelers of diseases in animals and humans. However, a number of aspects of plant epidemiology are very distinctive, and this leads to specific challenges in modelling plant diseases. Taking a single example, since plants are sessile the interplay between the spatial structure of the host population and pathogen dispersal becomes very important in setting epidemic dynamics, and including spatial structure in models is often necessary. A number of other aspects of the epidemiology of plant diseases are characteristic, and of course this sets a certain agenda for plant disease modelers. Here we outline a selection of twelve challenges, specific to modelling plant disease epidemiology that we feel are important targets for future work. Those challenges are: Linking epidemiological models to crop yield and ecosystem services; Temporal changes in host availability, from plant organs to populations; Capturing host spatial structure, even when data are limited; Beyond a single species: multiple and alternate hosts, spillover and community ecology; Realistic dispersal models, including meteorological and anthropomorphic drivers; Accounting for time-varying infectivity. Effects of vector preference on transmission; Beyond a single species: multiple strains, multiple pathogens and evolution; Using models to optimize detection; Optimizing dynamic controls in heterogeneous systems; Accounting for economics: moving optimal control theory to realistic landscapes; and Use of models by policy makers and stakeholders.