Analysis of disease progress as a basis for evaluating disease management practices.
The relationship between epidemiology and disease management is long-standing but sometimes tenuous. It may seem self-evident that improved understanding of epidemic processes will lead to more effective control practices but this remains a testable proposition rather than demonstrated reality. A wide range of models differing in mathematical sophistication and computational complexity has been proposed as a means of achieving a greater understanding of epidemiology and carrying this through to improved management. The potential exists to align these modeling approaches to evaluation of control practices and prediction of the consequent epidemic outcomes, but these have yet to make a major impact on practical disease management. For the immediate future simpler pragmatic approaches for analysis of disease progress, using nonlinear growth functions and/or integrated measures such as area under disease progress curves, will play a key role in informing tactical and strategic decisions on control treatments. These approaches have proved useful in describing control effectiveness and, in some cases, optimizing or changing control practices.
Department of Agricultural Sciences, Imperial College London, Wye Campus, Ashford, Kent TN25 5AH, United Kingdom. m.jeger@imperial.ac.uk
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D000383:Agriculture / Q000379:methods / Q000639:trends; D008962:Models, Theoretical; D017720:Molecular Epidemiology; D010935:Plant Diseases / Q000235:genetics / Q000382:microbiology; D012988:Soil Microbiology