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Information in multiscale epidemiological models.
C. C. MUNDT (1), P. Skelsey (2), P. S. Ojiambo (3), K. A. Garrett (2). (1) Oregon State University, Corvallis, OR, U.S.A.; (2) Kansas State University, Manhattan, KS, U.S.A.; (3) North Carolina State University, Raleigh, NC, U.S.A.

Food, fiber, and energy demands over the next 50 years call for substantial increases in agricultural productivity while addressing crucial issues of environmental impact and resource sustainability. One approach to this challenge is to design agricultural landscapes that are resistant to the establishment and spread of plant disease epidemics. pidemic processes occur over different spatial scales, ranging from infection of an individual plant cell to the dispersal of propagules within and between continents. In contrast, practical limitations usually restrict epidemiological studies to a much narrower spatial scale. Thus, there is a great need to better understand how epidemic processes translate across disparate spatial scales. We will discuss modeling approaches that make predictions across spatial scales that are relevant to the influence of landscape heterogeneity on disease spread. The focus will be on the degree to which processes may be scale-dependent versus scale-neutral, how the issue of scale influences the impact of landscape heterogeneity on prediction of epidemic spread, and the degree to which predictions of different models are congruent. One clear conclusion of the modeling efforts is that there is a crucial lack of biological data to adequately parameterize and validate currently available models.<p><p>Keywords:

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