## 3 Results  
The input data structure and preprocessing steps (e.g., outlier removal, dataset splitting) follow standard machine-learning practices consistent with prior work [38], while the Random Forest classifier was selected via a new comparative validation specific to this predictive task. The optimization (performed on data between 1 January 2020 and 30 September 2021) was based on a ten-fold validation protocol, considering the overall values of accuracy, specificity, sensitivity, and f1 score for the simple and weighted average across all the labels, computing the median value, the 1 st and 3 rd quartile, and the 95% confidence interval. A single final score was computed with an equal weighting of all the metrics and their distributions; the complete weights matrix and the table of complete results are reported in Tables 1 and 2. The best performance was obtained with a random forest classifier, considering the time-series plus all the additional attributes. Validation results are reported in Table 3. \begin{table}
\begin{tabular}{c c c c} \hline
**Methods included/Machine** & & & \\
**Learning Algorithm** & **Random Forest** & **Support Vector Machine** & **Logistic Regression** \\ \hline All & **0.6282** & 0.5969 & 0.5878 \\ TIME-SERIES (TS) & 0.6275 & 0.5995 & 0.5815 \\ DERIVED ATTRIBUTES & 0.6256 & 0.5955 & 0.5727 \\ AMBULANCE DISPATCHCHES & 0.6223 & 0.5961 & 0.5726 \\ EMERGENCY CALLS & 0.622 & 0.5951 & 0.5723 \\ MAX-MIN + TS & 0.6219 & 0.5957 & 0.5724 \\ MAX-MIN & 0.6216 & 0.5959 & 0.573 \\ STATISTICS + TS & 0.6211 & 0.5955 & 0.5729 \\ STATISTICS & 0.6211 & 0.5977 & 0.5773 \\ POSITION AND STATISTICS + TS & 0.621 & 0.5819 & 0.5584 \\ POSITION AND STATISTICS & 0.621 & 0.5828 & 0.5621 \\ LIN REGRESSION + TS & 0.621 & 0.5828 & 0.5625 \\ LIN REGRESSION & 0.621 & 0.5827 & 0.5624 \\ EXP REGRESSION + TS & 0.621 & 0.5828 & 0.5624 \\ EXP REGRESSION & 0.621 & 0.5831 & 0.5627 \\ LIN AND EXP REGRESSION & 0.6209 & 0.5947 & 0.5722 \\ POSITION + LIN REG + TS & 0.6208 & 0.583 & 0.5629 \\ POSITION + LIN REG & 0.6205 & 0.5955 & 0.5732 \\ POSITION + EXP REG + TS & 0.6205 & 0.5825 & 0.5607 \\ POSITION + EXP REG & 0.6203 & 0.5829 & 0.5618 \\ POSITION + LIN AND EXP REG + TS & 0.6203 & 0.583 & 0.5632 \\ POSITION + LIN AND EXP REG & 0.6203 & 0.5829 & 0.5634 \\ STATISTICS + LIN REG + TS & 0.6199 & 0.5823 & 0.5591 \\ STATISTICS + LIN REG & 0.6198 & 0.5963 & 0.5744 \\ STATISTICS + EXP REG + TS & 0.6198 & 0.5963 & 0.5744 \\ STATISTICS + EXP REG & 0.6187 & 0.5824 & 0.5606 \\ STATISTICS + LIN AND EXP REG + TS & 0.6187 & 0.582 & 0.5594 \\ STATISTICS + LIN AND EXP REG & 0.6172 & 0.5995 & 0.5821 \\ \hline \end{tabular}
\end{table} 
Table 2: Optimization of a machine learning algorithm for classification of territorial districts (with approximatively 100,000 residents) into prediction classes representing the foreseen scenario in terms of vehicles to be dispatched by the EMS department (for respiratory and infective issues) during the 7 days following the classification, considered representative of the diffusion of COVID-19 epidemic in the target territory of Lombardy region, Italy, between January 2020 and December 2021; the table reports the final scores of different combinations of machine learning algorithms and attributes combinations, computed by averaging the different metrics (and their distributions indicators) on the hold-out validation protocol, according to the defined weighting (see Table 1); the highest performance metric value obtained is highlighted in bold. \begin{table}
\begin{tabular}{c c c c} \hline
**Weights Assigned to** & & & \\
**Median (Ist Quartile-3 rd Quartile)** & & & \\
**Confidence Interval 95\%** & & & \\
**(Lower-Upper)** & & & \\ \hline ACCURACY & NA & NA & 0.17 (0.0425–0.0425) \\  & 0.055 (0.0138–0.0138) & 0.055 (0.0138–0.0138) & C.1 95\% (0.0425–0.0425) \\ MACRO AVERAGE & C.1 95\% (0.0138–0.0138) & 0.055 (0.0138–0.0138) & C.1 95\% (0.0138–0.0138) \\ WEIGHTED AVERAGE & C.1 95\% (0.0138–0.0138) & 0.055 (0.0138–0.0138) & C.1 95\% (0.0138–0.0138) \\ \hline \end{tabular}
\end{table}