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Modelling virus coinfection to inform management of maize lethal necrosis in Kenya

Overview of attention for article published in Phytopathology, May 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#2 of 1,658)
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

news
12 news outlets
blogs
1 blog
twitter
17 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
6 Dimensions

Readers on

mendeley
1 Mendeley
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Title
Modelling virus coinfection to inform management of maize lethal necrosis in Kenya
Published in
Phytopathology, May 2017
DOI 10.1094/phyto-03-17-0080-fi
Pubmed ID
Authors

Dr. Frank Hilker, Dr. Linda Allen, Dr. Vrushali Bokil, Dr. Cheryl Briggs, Dr. Zhilan Feng, Prof. Karen A. Garrett, Dr. Louis Gross, Dr. Frédéric Hamelin, Dr. Michael J Jeger, Dr. Carrie Manore, Dr. Alison Power, Dr. Margaret Redinbaugh, Dr. Megan Rúa, Dr. Nik J Cunniffe, Hilker, Frank, Allen, Linda, Bokil, Vrushali, Briggs, Cheryl, Feng, Zhilan, Garrett, Karen A, Gross, Louis, Hamelin, Frédéric, Jeger, Michael J, Manore, Carrie, Power, Alison, Redinbaugh, Margaret, Rúa, Megan, Cunniffe, Nik J, Frank M. Hilker, Linda J. S. Allen, Vrushali A. Bokil, Cheryl J. Briggs, Zhilan Feng, Karen A. Garrett, Louis J. Gross, Frédéric M. Hamelin, Michael J. Jeger, Carrie A. Manore, Alison G. Power, Margaret G. Redinbaugh, Megan A. Rúa, Nik J. Cunniffe

Abstract

Maize lethal necrosis (MLN) has emerged as a serious threat to food security in sub-Saharan Africa. MLN is caused by coinfection with two viruses, maize chlorotic mottle virus (MCMV) and a potyvirus, often sugarcane mosaic virus (SCMV). To better understand dynamics of MLN and to provide insight into disease management, we model the spread of the viruses causing MLN within and between growing seasons. The model allows for transmission via vectors, soil and seeds, as well as exogenous sources of infection. Following model parameterisation, we predict how management affects disease prevalence and crop performance over multiple seasons. Resource-rich farmers with large holdings can achieve good control by combining clean seed and insect control. Crop rotation is often required to effect full control, however. Resource-poor farmers with smaller holdings must rely on rotation and roguing, and achieve more limited control. For both types of farmer, unless management is synchronised over large areas, exogenous sources of infection can thwart control. As well as providing practical guidance, our modelling framework is potentially informative for other cropping systems in which coinfection has devastating effects. Our work also emphasises how mathematical modelling can inform management of an emerging disease even when epidemiological information remains scanty.

Twitter Demographics

The data shown below were collected from the profiles of 17 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 100%
Readers by discipline Count As %
Mathematics 1 100%

Attention Score in Context

This research output has an Altmetric Attention Score of 104. 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 17 February 2018.
All research outputs
#107,703
of 11,335,350 outputs
Outputs from Phytopathology
#2
of 1,658 outputs
Outputs of similar age
#6,275
of 265,148 outputs
Outputs of similar age from Phytopathology
#1
of 50 outputs
Altmetric has tracked 11,335,350 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,658 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done particularly well, scoring higher than 99% 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 265,148 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 97% of its contemporaries.
We're also able to compare this research output to 50 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 98% of its contemporaries.