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Durable Resistance to Crop Pathogens: An Epidemiological Framework to Predict Risk under Uncertainty

Overview of attention for article published in PLoS Computational Biology, January 2013
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

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4 news outlets
twitter
1 X user
facebook
1 Facebook page

Readers on

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80 Mendeley
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Article details
Title
Durable Resistance to Crop Pathogens: An Epidemiological Framework to Predict Risk under Uncertainty
Published in
PLoS Computational Biology, January 2013
DOI 10.1371/journal.pcbi.1002870
Pubmed ID
Authors
Abstract

Increasing the durability of crop resistance to plant pathogens is one of the key goals of virulence management. Despite the recognition of the importance of demographic and environmental stochasticity on the dynamics of an epidemic, their effects on the evolution of the pathogen and durability of resistance has not received attention. We formulated a stochastic epidemiological model, based on the Kramer-Moyal expansion of the Master Equation, to investigate how random fluctuations affect the dynamics of an epidemic and how these effects feed through to the evolution of the pathogen and durability of resistance. We focused on two hypotheses: firstly, a previous deterministic model has suggested that the effect of cropping ratio (the proportion of land area occupied by the resistant crop) on the durability of crop resistance is negligible. Increasing the cropping ratio increases the area of uninfected host, but the resistance is more rapidly broken; these two effects counteract each other. We tested the hypothesis that similar counteracting effects would occur when we take account of demographic stochasticity, but found that the durability does depend on the cropping ratio. Secondly, we tested whether a superimposed external source of stochasticity (for example due to environmental variation or to intermittent fungicide application) interacts with the intrinsic demographic fluctuations and how such interaction affects the durability of resistance. We show that in the pathosystem considered here, in general large stochastic fluctuations in epidemics enhance extinction of the pathogen. This is more likely to occur at large cropping ratios and for particular frequencies of the periodic external perturbation (stochastic resonance). The results suggest possible disease control practises by exploiting the natural sources of stochasticity.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Peru 1 1%
Unknown 79 99%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 20 25%
Researcher 16 20%
Student > Master 9 11%
Student > Bachelor 7 9%
Student > Postgraduate 3 4%
Other 8 10%
Unknown 17 21%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 37 46%
Biochemistry, Genetics and Molecular Biology 7 9%
Medicine and Dentistry 5 6%
Environmental Science 3 4%
Computer Science 3 4%
Other 4 5%
Unknown 21 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 35. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 August 2025.
All research outputs
#1,303,871
of 29,066,836 outputs
Outputs from PLoS Computational Biology
#907
of 9,628 outputs
Outputs of similar age
#9,975
of 307,578 outputs
Outputs of similar age from PLoS Computational Biology
#8
of 125 outputs
Altmetric has tracked 29,066,836 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,628 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.4. This one has done particularly well, scoring higher than 90% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 307,578 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 125 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.