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Evaluation of Probabilistic Disease Forecasts.
Gareth Hughes1, Fiona J Burnett1
1Crop and Soil Systems Research Group, SRUC, Edinburgh EH9 3JG, U.K.
Phytopathology
|May 5, 2017
Summary
Evaluating probabilistic disease forecasts requires predictive values, not just sensitivity and specificity. Scoring rules offer a principled method for assessing forecast accuracy in disease management, with interpretable components.
Area of Science:
- Agricultural science
- Statistics
- Epidemiology
Background:
- Probabilistic disease forecasts are crucial for effective disease management.
- Traditional evaluation metrics like sensitivity and specificity are conditional on disease status.
- Predictive values, conditional on forecast results, are vital for decision-making but less commonly reported.
Purpose of the Study:
- To discuss the application of scoring rules for evaluating probabilistic disease forecasts.
- To consider an index of separation and its relation to scoring rules in this context.
- To highlight the utility of scoring rules in plant disease management.
Main Methods:
- Statistical evaluation of probabilistic forecasts.
- Application of scoring rules.
- Analysis of an index of separation.
Main Results:
- Scoring rules provide a principled framework for evaluating probabilistic forecasts.
- The decomposition of scoring rules yields interpretable components, beneficial for disease forecast evaluation.
- An index of separation is related to scoring rules.
Conclusions:
- Scoring rules are advantageous for evaluating probabilistic forecasts in plant disease management.
- Their interpretable components enhance the assessment of forecast accuracy.
- A comprehensive evaluation should consider metrics beyond sensitivity and specificity.
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