Information graphs incorporating predictive values of disease forecasts

Gareth Hughes*, Jennifer Reed, Neil McRoberts

*Corresponding author for this work

Research output: Contribution to journalArticle

1 Citation (Scopus)
1 Downloads (Pure)

Abstract

Diagrammatic formats are useful for summarizing the processes of evaluation and comparison of forecasts in plant pathology and other disciplines where decisions about interventions for the purpose of disease management are often based on a proxy risk variable. We describe a new diagrammatic format for disease forecasts with two categories of actual status and two categories of forecast. The format displays relative entropies, functions of the predictive values that characterize expected information provided by disease forecasts. The new format arises from a consideration of earlier formats with underlying information properties that were previously unexploited. The new diagrammatic format requires no additional data for calculation beyond those used for the calculation of a receiver operating characteristic (ROC) curve. While an ROC curve characterizes a forecast in terms of sensitivity and specificity, the new format described here characterizes a forecast in terms of relative entropies based on predictive values. Thus it is complementary to ROC methodology in its application to the evaluation and comparison of forecasts.

Original languageEnglish
Article number361
JournalEntropy
Volume22
Issue number3
Early online date20 Mar 2020
DOIs
Publication statusFirst published - 20 Mar 2020

Keywords

  • Diagnostic information
  • Forecast
  • Likelihood ratio
  • Negative predictive value
  • Positive predictive value
  • Probability
  • Relative entropy

Fingerprint Dive into the research topics of 'Information graphs incorporating predictive values of disease forecasts'. Together they form a unique fingerprint.

Cite this