Case definition and desired accuracy
The accuracy of EVI is measured by its sensitivity \(\left(Se\right)\) (i.e., the probability of correctly issuing an early warning for an upcoming epidemic wave) and its specificity \(\left(Sp\right)\) (i.e., the probability of not signaling a false alarm in the absence of upcoming waves) and depends on the case definition of what constitutes a noteworthy rise in the expected number of cases to be considered as the indicative of an epidemic wave. For example, a case definition can be, as in the example application that follows, a rise in the mean number of cases between two consecutive weeks higher than 20%.
For a specified case definition, the accuracy of EVI depends on the window size m and the threshold c, which should be selected in a way to achieve a desired accuracy target. One option is the selection of m and c values that lead to the simultaneous optimization of Se and Sp for EVI and the maximization of the Youden index \(\left(J=Se+Sp-1\right)\)\cite{Fluss_2005} and, hence, the overall minimization of the false results (i.e. both false positive and false negative early warnings). Another approach could be to select \(m\) and \(c\) such that the highest \(Se\left(or\ Sp\right)\) is achieved with \(Sp\left(or\ Se\right)=1\) or not dropping below a critical value (e.g. 95%). Advanced Receiver Operating Characteristic curve analysis can also be performed \cite{Zweig_1993} and selection of critical values can be based on indices that quantify the relative cost of false positive (i.e., falsely predicting an upcoming epidemic wave) to false negative (i.e., failing to predict an upcoming epidemic wave) warnings, like the misclassification cost term (MCT).