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Title: Modeling Virus Coinfection to Inform Management of Maize Lethal Necrosis in Kenya

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. Finally, our work also emphasizes how mathematical modeling can inform management of an emerging disease even when epidemiological information remains scanty.
Authors:
 [1] ;  [2] ;  [3] ;  [4] ;  [5] ;  [6] ;  [7] ;  [8] ;  [9] ; ORCiD logo [10] ;  [11] ;  [12] ;  [13] ;  [14]
  1. Osnabruck Univ., Osnabruck (Germany)
  2. Texas Tech Univ., Lubbock, TX (United States)
  3. Oregon State Univ., Corvallis, OR (United States)
  4. Univ. of California, Santa Barbara, CA (United States)
  5. Purdue Univ., West Lafayette, IN (United States)
  6. Univ. of Florida, Gainesville, FL (United States)
  7. Univ. of Tennessee, Knoxville, TN (United States)
  8. Univ. de Rennes 1, Univ. Bretagne-Loire, Rennes (France)
  9. Imperial College London, London (United Kingdom)
  10. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  11. Cornell Univ., Ithaca, NY (United States)
  12. Ohio State Univ., Wooster, OH (United States)
  13. Wright State Univ., Dayton, OH (United States)
  14. Univ. of Cambridge, Cambridge (United Kingdom)
Publication Date:
Report Number(s):
LA-UR-17-22310
Journal ID: ISSN 0031-949X
Grant/Contract Number:
AC52-06NA25396
Type:
Accepted Manuscript
Journal Name:
Phytopathology
Additional Journal Information:
Journal Volume: 107; Journal Issue: 10; Journal ID: ISSN 0031-949X
Publisher:
American Phytopathological Society
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE Laboratory Directed Research and Development (LDRD) Program
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 59 BASIC BIOLOGICAL SCIENCES; Biological Science; Mathematics
OSTI Identifier:
1409767

Hilker, Frank M., Allen, Linda J. S., Bokil, Vrushali A., Briggs, Cheryl J., Feng, Zhilan, Garrett, Karen A., Gross, Louis J., Hamelin, Frédéric M., Jeger, Michael J., Manore, Carrie A., Power, Alison G., Redinbaugh, Margaret G., Rúa, Megan A., and Cunniffe, Nik J.. Modeling Virus Coinfection to Inform Management of Maize Lethal Necrosis in Kenya. United States: N. p., Web. doi:10.1094/PHYTO-03-17-0080-FI.
Hilker, Frank M., Allen, Linda J. S., Bokil, Vrushali A., Briggs, Cheryl J., Feng, Zhilan, Garrett, Karen A., Gross, Louis J., Hamelin, Frédéric M., Jeger, Michael J., Manore, Carrie A., Power, Alison G., Redinbaugh, Margaret G., Rúa, Megan A., & Cunniffe, Nik J.. Modeling Virus Coinfection to Inform Management of Maize Lethal Necrosis in Kenya. United States. doi:10.1094/PHYTO-03-17-0080-FI.
Hilker, Frank M., Allen, Linda J. S., Bokil, Vrushali A., Briggs, Cheryl J., Feng, Zhilan, Garrett, Karen A., Gross, Louis J., Hamelin, Frédéric M., Jeger, Michael J., Manore, Carrie A., Power, Alison G., Redinbaugh, Margaret G., Rúa, Megan A., and Cunniffe, Nik J.. 2017. "Modeling Virus Coinfection to Inform Management of Maize Lethal Necrosis in Kenya". United States. doi:10.1094/PHYTO-03-17-0080-FI. https://www.osti.gov/servlets/purl/1409767.
@article{osti_1409767,
title = {Modeling Virus Coinfection to Inform Management of Maize Lethal Necrosis in Kenya},
author = {Hilker, Frank M. and Allen, Linda J. S. and Bokil, Vrushali A. and Briggs, Cheryl J. and Feng, Zhilan and Garrett, Karen A. and Gross, Louis J. and Hamelin, Frédéric M. and Jeger, Michael J. and Manore, Carrie A. and Power, Alison G. and Redinbaugh, Margaret G. and Rúa, Megan A. and Cunniffe, Nik J.},
abstractNote = {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. Finally, our work also emphasizes how mathematical modeling can inform management of an emerging disease even when epidemiological information remains scanty.},
doi = {10.1094/PHYTO-03-17-0080-FI},
journal = {Phytopathology},
number = 10,
volume = 107,
place = {United States},
year = {2017},
month = {8}
}