Modeling Virus Coinfection to Inform Management of Maize Lethal Necrosis in Kenya
File(s)
Published version
Author(s)
Type
Journal Article
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 and a potyvirus, often Sugarcane
mosaic virus. To better understand the dynamics of MLN and to provide
insight into disease management, we modeled the spread of the viruses
causing MLN within and between growing seasons. The model allows for
transmission via vectors, soil, and seed, as well as exogenous sources of
infection. Following model parameterization, 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. However, crop
rotation is often required to effect full control. 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
synchronized over large areas, exogenous sources of infection can thwart
control. As well as providing practical guidance, our modeling framework
is potentially informative for other cropping systems in which coinfection
has devastating effects. Our work also emphasizes how mathematical
modeling can inform management of an emerging disease even when
epidemiological information remains scanty.
security in sub-Saharan Africa. MLN is caused by coinfection with two
viruses, Maize chlorotic mottle virus and a potyvirus, often Sugarcane
mosaic virus. To better understand the dynamics of MLN and to provide
insight into disease management, we modeled the spread of the viruses
causing MLN within and between growing seasons. The model allows for
transmission via vectors, soil, and seed, as well as exogenous sources of
infection. Following model parameterization, 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. However, crop
rotation is often required to effect full control. 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
synchronized over large areas, exogenous sources of infection can thwart
control. As well as providing practical guidance, our modeling framework
is potentially informative for other cropping systems in which coinfection
has devastating effects. Our work also emphasizes how mathematical
modeling can inform management of an emerging disease even when
epidemiological information remains scanty.
Date Issued
2017-10-01
Date Acceptance
2017-05-19
Citation
Phytopathology, 2017, 107 (10), pp.1095-1108
ISSN
0031-949X
Publisher
American Phytopathological Society
Start Page
1095
End Page
1108
Journal / Book Title
Phytopathology
Volume
107
Issue
10
Copyright Statement
Copyright © 2017 The Author(s). This is an open access article
distributed under the CC BY-NC-ND 4.0 International license.
distributed under the CC BY-NC-ND 4.0 International license.
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/28535127
Subjects
Science & Technology
Life Sciences & Biomedicine
Plant Sciences
CHLOROTIC-MOTTLE-VIRUS
SUGARCANE-MOSAIC-VIRUS
SUB-SAHARAN AFRICA
SEED-TRANSMISSION
PLANT-PATHOGENS
DISEASE-CONTROL
FOOD CROPS
1ST REPORT
CORN
MACHLOMOVIRUS
Publication Status
Published
Coverage Spatial
United States