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A generalized linear modeling approach for characterizing disease incidence in a spatial hierarchy
Phytopathology
|October 24, 2008
Summary
Statistical models quantify disease incidence heterogeneity across spatial scales. New models accurately predict disease spread in strawberry leaf blight, improving sampling strategies.
Area of Science:
- Plant pathology
- Statistical modeling
- Epidemiology
Background:
- Understanding spatial heterogeneity is crucial for accurate disease incidence assessment.
- Previous models focused on two-scale hierarchies, limiting applications in complex systems.
Purpose of the Study:
- To develop and validate statistical models for disease incidence in a three-scale spatial hierarchy.
- To quantify the impact of spatial heterogeneity on disease relationships.
- To improve disease sampling and interpretation methods.
Main Methods:
- Extension of existing statistical theory for a three-scale hierarchy (sampling unit, leaf, leaflet).
- Application of beta-binomial distribution and modified binomial functions to model disease aggregation.
- Utilized nonlinear models and generalized linear models with complementary log-log transformation.
Main Results:
- Disease incidence at the sampling unit scale follows a saturation curve with increasing leaf/leaflet incidence.
- Beta-binomial distribution accurately fits leaflet and leaf disease data.
- Nonlinear models effectively described disease aggregation without curve fitting.
- Generalized linear models provided accurate predictions, with minimal improvement from curve fitting.
Conclusions:
- The developed models accurately predict disease incidence across spatial scales.
- These models offer broad applicability for disease incidence sampling and interpretation.
- Results enhance understanding of how diseased individuals aggregate within sampling units.
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