Evaluation of probabilistic disease forecasts

G Hughes, FJ Burnett

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
22 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)1136 - 1143
Number of pages8
Issue number10
Early online date14 Jul 2017
Publication statusFirst published - 14 Jul 2017

Bibliographical note



  • Brier score
  • Divergence score
  • Expected mutual information
  • G2 test
  • McFadden's R2
  • PSEP
  • Reliability
  • Resolution
  • Uncertainty


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