Statistical models have been used extensively for summarizing and comparing plant disease epidemics. In these exercises, the statistical package R was used to illustrate models and to summarize disease progress over time, both in terms of population growth models and the AUDPC.
Understanding temporal disease progress lays the groundwork for developing methods to manage plant disease, such as developing forecasting models (e.g., determining the optimal time to apply a fungicide spray) or selecting an optimal planting date to reduce disease impacts.
If you would like more experience with R in the context of epidemiology, see our other exercises for the study of dispersal (Esker et al. 2007), analysis of spatial pattern (Sparks et al. 2008), and disease forecasting (Esker et al. 2008). For other plant disease epidemiology exercises see Francl and Neher (1997) and for general exercises in ecology and epidemiology see Donovan and Weldon (2002).
Acknowledgments
This document was prepared as part of a course in the Ecology and Epidemiology of Plant Pathogens at Kansas State University. Sparks and Esker were the lead writers of the document, Garrett was faculty adviser, and student contributors appear in alphabetical order. We appreciate the very helpful comments of P. Garfinkel, S. Pethybridge, J. Yuen, PHI reviewers, and members of the KSU course. It is also a pleasure to acknowledge support by the U.S. National Science Foundation under Grants DEB-0130692, DEB-0516046, EF-0525712 (as part of the joint NSF-NIH Ecology of Infectious Disease program) and DBI-0630726, by the Ecological Genomics Initiative of Kansas through NSF Grant No. EPS-0236913 with matching funds from the Kansas Technology Enterprise Corporation, by the Office of Science (Program in Ecosystem Research), U.S. Department of Energy, Grant No. DE-FG02-04ER63892, by the U.S. Agency for International Development for the Sustainable Agriculture and Natural Resources Management Collaborative Research Support Program (SANREM CRSP) under terms of Cooperative Agreement Award No. EPP-A-00-04-00013-00 to the Office of International Research and Development at Virginia Tech and for the Integrated Pest Management CRSP, by USDA grant 2002-34103-11746, and by the NSF Long Term Ecological Research Program at Konza Prairie. This is Kansas State Experiment Station Contribution No. 07-309-J.
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