Evaluation of Probabilistic Disease Forecasts

• Published in: Phytopathology Vol. 107; no. 10; pp. 1136 - 1143
• Main Authors: Hughes, Gareth, Burnett, Fiona J
• Format: Journal Article
• Language: English
• Published: United States 01.10.2017
• Subjects: Decision Making; Forecasting; Models, Theoretical; Plant Diseases - prevention & control; Plant Diseases - statistics & numerical data; Probability; expected mutual information; G2 test; McFadden’s R2; reliability; divergence score; uncertainty; resolution; Brier score
• ISSN: 0031-949X; 1943-7684
• DOI: 10.1094/PHYTO-01-17-0023-FI

The statistical evaluation of probabilistic disease forecasts often involves calculation of metrics defined conditionally on disease status, such as sensitivity and specificity. However, for the purpose of disease management decision making, metrics defined conditionally on the result of the forecast-predictive values-are also important, although less frequently reported. In this context, the application of scoring rules in the evaluation of probabilistic disease forecasts is discussed. An index of separation with application in the evaluation of probabilistic disease forecasts, described in the clinical literature, is also considered and its relation to scoring rules illustrated. Scoring rules provide a principled basis for the evaluation of probabilistic forecasts used in plant disease management. In particular, the decomposition of scoring rules into interpretable components is an advantageous feature of their application in the evaluation of disease forecasts.