Every time a new time point \(t\) is observed, the model uses all of the observed cases up to \(t\) to decide whether it should issue an early warning, at time point \(t\). The steps are: 
  1. Observed cases up to \(t\) are analyzed for all possible values of the window size \(\left(m\in\left[1,m_{\max}\right]\right)\) and threshold \(\left(c\in\left[0,1\right]\right)\)
  2. For each of the \(m\ and\ c\) combinations, the \(Se_{t_{m,c}}\)and \(Sp_{t_{m,c}}\)are estimated for the predefined case definition (Eq. 4). 
  3. The  \(m'\) and \(c'\) that give the best \(Se_{t_{m',c'}}\) and \(Sp_{t_{m',c'}}\) combination are selected.
  4. For \(m'\) and \(c'\), the value of \(Ind_{EVI_{t,t-1}}\) is determined at the most recent time point \(t\) and a decision is made on whether or not a warning signal is issued.