Modeling Virus Coinfection to Inform Management of Maize Lethal Necrosis in Kenya
Frank M Hilker1, Linda J S Allen1, Vrushali A Bokil1
1First author: Institute of Environmental Systems Research, School of Mathematics/Computer Science, Osnabrück University, 49069 Osnabrück, Germany; second author: Department of Mathematics and Statistics, Texas Tech University, Lubbock 79409; third author: Department of Mathematics, Oregon State University, Corvallis 97331; fourth author: Department of Ecology, Evolution and Marine Biology, University of California, Santa Barbara 93106; fifth author: Department of Mathematics, Purdue University, West Lafayette, IN 47907; sixth author: Plant Pathology Department, Institute for Sustainable Food Systems, and Emerging Pathogens Institute, University of Florida, Gainesville 32611; seventh author: National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville 37996; eighth author: IGEPP, Agrocampus Ouest, INRA, Université de Rennes 1, Université Bretagne-Loire, 35000 Rennes, France; ninth author: Centre for Environmental Policy, Imperial College London, Ascot SL5 7PY, United Kingdom; tenth author: Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87544; eleventh author: Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY 14853; twelfth author: United States Department of Agriculture-Agricultural Research Service Corn, Soybean and Wheat Quality Research Unit and Department of Plant Pathology, Ohio State University, Wooster 44691; thirteenth author: Department of Biological Sciences, Wright State University, Dayton, OH 45435; and fourteenth author: Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, United Kingdom.
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. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
