\[\sigma_t=\sqrt{\frac{1}{m}\sum_{i_t=1}^m\left(x_{i_t}-\overline{\mu}_t\right)}\]
with \(\overline{\mu_t}\) the mean of the \(t^{th}\) window. Subsequently, \(EVI\) is calculated as the relative change of \(\left(\sigma_t\right)\) between two consecutive rolling windows:
\[EVI_{t-1,t}=\frac{\sigma_t-\sigma_{t-1}}{\sigma_t}\]
    We expect an increase in the future number of cases, if \(EVI_{t-1,t}\) exceeds a threshold \(c\) \(\left(c\in\left[0,1\right]\right)\) and the observed cases at time point \(t,\left(y_t\right)\) are higher than the average of the reported cases in the previous week:
\[Ind_{EVI_{t-1,t}} = \begin{cases} 1 \;\;\; if \;\;\; EVI_{t-1,t}>=c \;\; \wedge \;\; y_t>=\overline{\mu}_{t:t-7} \\ 0 \;\;\; \text{otherwise} \end{cases} \]