Contents

• Introduction
• Case Study #1: Sting nematode (Belonolaimus longicaudatus) on turf
• AUDPC
  • Case Study #2: Stripe rust (Puccinia striiformis) on wheat
• Linear Regression in R
• Nonlinear Regression in R
• Using Growth Models in Plant Pathology
  • Case Study #3: Citrus Canker
  • Case Study #4: Cassava mosaic disease
• Conclusions
• Suggested Quiz and Study Questions
• References

Sparks, A.H., P.D. Esker, M. Bates, W. Dall' Acqua, Z. Guo, V. Segovia, S.D. Silwal, S. Tolos, and K.A. Garrett, 2008. Ecology and Epidemiology in R: Disease Progress over Time. The Plant Health Instructor. DOI:10.1094/PHI-A-2008-0129-02.

Ecology and Epidemiology in R: Disease Progress over Time

Quiz

1. If you need to compare disease progress and severity in two different years, how can you use R to do that?
2. We know from reading about Xanthamonas axonopodis pv. citri that there is a direct relationship between spread of this pathogen and wind-driven rain, particularly, rain events associated with wind speeds greater than 8 ms^-1. Do any of the models discussed in this section reflect that relationship? If so, which ones?
3. When doing non-linear regression in R why do we need the initial value for the parameter estimates?
4. How can an insect infestation influence the rate at which disease progresses over time for plant pathogens? Are there ways to account for any interaction using the models depicted in this document?
5. Why is the study of disease progress over time important? What answers does this type of research provide?
6. You have read about AUDPC, what does it mean in terms of the disease? Why is it important?
7. Is it possible to predict how plant resistance to a disease affects disease progress over time? How would you do that? What kind of model would you use?
8. The exponential model assumes that the absolute rate of disease increase (dy/dt) is proportional to the level of disease present (y), whereas the monomolecular model assumes the absolute rate of change is proportional to the healthy tissue (1-y). What is the shape of these curves and how does this affect their usefulness in different situations?
9. The Weibull model is said to be flexible as it contains a larger number of parameters. Give an estimate of the parameters a, b, and c in the Weibull model that could approximate the example of a logistic model in the document?
10. When doing a simple linear regression, why is model checking necessary?