Bayesian analysis of plant disease prediction

Rule-based systems for the prediction of the occurrence of disease can be evaluated in a number of different ways. One way is to examine the probability of disease occurrence before and after using the predictor. Bayes's Theorem can be a useful tool to examine how a disease forecast (either positive or negative) affects the probability of occurrence, and simple analyses can be conducted without knowing the risk preferences of the targeted decision makers. Likelihood ratios can be calculated from the sensitivity and specificity of the forecast, and provide convenient summaries of the forecast performance. They can also be used in a simpler form of Bayes's Theorem. For diseases where little or no prior information on occurrence is available, most forecasts will be useful in that they will increase or decrease the probability of disease occurrence. For extremely common or extremely rare diseases, likelihood ratios may not be sufficiently large or small to substantially affect the probability of disease occurrence or make any difference to the actions taken by the decision maker.