## 3 Results

Table 1: 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) 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. The proposed predictive model is accurately forecasting epidemic phases in 78.9% of cases considering the two most likely scenarios indicated by the double labeling system. Performance is particularly high for no diffusion scenarios, with 76.3% of cases correctly identified by the first label and 88.8% captured within the top two. Even under active spreading conditions, the system maintains solid predictive capacity, reaching 70.1% accuracy within the top two predictions and achieving 96.9% when including adjacent (near-miss) classifications. Misclassifications entirely unrelated to predicted scenarios remain rare, with only 4.3% of cases falling into the worst prediction category, and just 3.1% during \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Absolute Frequencies (_n_)**} & \multicolumn{6}{c}{**Relative Frequencies (\%)**} \\
**LAB** & \(n\) & **1** & **2** & **3** & **4** & **5** & **1** & **2** & **3** & **4** & **5** \\ \hline
11 & 26,399 & 25,329 & 1080 & **3** & **7** & **10** & 95.857 & 4.09\% & 0.01\% & 0.03\% & 0.04\% \\
12 & 34,812 & 24,511 & 9729 & 148 & 322 & 302 & 69.34\% & 27.95\% & 0.43\% & 0.92\% & 0.87\% \\ \hline
21 & 17,900 & 6264 & 10,211 & **451** & **498** & **476** & 34.99\% & 57.14\% & 2.52\% & 2.78\% & 2.66\% \\
22 & 31,920 & 5476 & 24,515 & 572 & 587 & 740 & 17.16\% & 75.03\% & 1.79\% & 1.84\% & 2.32\% \\
25 & 28,135 & 2094 & 16,526 & 3842 & 4034 & 16.22 & 7.44\% & 65.05\% & 13.66\% & 14.34\% & 16.5\% \\ \hline
33 & 26,319 & 20 & 157 & 13,458 & 6049 & 1855 & 0.08\% & 0.60\% & 65.01\% & 22.98\% & 2.05\% \\
34 & 19,061 & 55 & 588 & **375** & **6496** & 3167 & 0.29\% & 3.08\% & 45.9\% & 54.18\% & 16.62\% \\
35 & 20,089 & 103 & 1079 & 852 & 5375 & 5170 & 0.51\% & 5.37\% & 41.62\% & 26.76\% & 25.74\% \\ \hline
43 & 13,929 & 53 & 346 & 1916 & 5683 & 2946 & 0.38\% & 2.48\% & 35.29\% & 60.05\% & 21.15\% \\
44 & 22,366 & 15 & 93 & 5121 & 13.57\% & 3565 & 0.07\% & 0.42\% & 22.90\% & 60.05\% & 15.94\% \\
45 & 17,359 & 146 & 716 & 4234 & **372** & 5536 & 0.84\% & 4.12\% & 24.39\% & 58.76\% & 31.89\% \\ \hline
53 & 17,486 & 90 & 803 & 4530 & 4682 & 7831 & 0.51\% & 4.59\% & 25.91\% & 26.78\% & 19.41\% \\
54 & 21,242 & 180 & 1033 & 3548 & 6479 & 10.00\% & 0.85\% & 4.86\% & 16.70\% & 30.50\% & 47.05\% \\
55 & 23,583 & **14** & **217** & 1400 & 3624 & 18.32\% & 0.06\% & 0.92\% & 5.94\% & 15.37\% & 77.72\% \\ \hline \hline  & & \multicolumn{6}{c}{ALL RECORDS} & \multicolumn{6}{c}{NO DIFFUSION} & \multicolumn{6}{c}{ACTIVE SPREADING} \\
**Prediction level A** & & 60.83\% & & 76.83\% & & 54.1\% & \\ \hline Prediction level B & 18.10\% & & 12.47\% & & 19.63\% & \\ \hline Prediction level C & 78.81\% & & 65.88\% & & 58.88\% & & 20.8\% & \\ \hline Prediction level C & 18.14\% & & 5.11\% & & 26.82\% & \\ \hline Prediction level A + B + C & 97.04\% & & 93.92\% & & 96.90\% & \\ \hline Prediction level D & 4.30\% & & 6.08\% & & 3.10\% & \\ \hline Prediction level C + D & 22.43\% & & **11.20\%** & & **29.92\%** & \\ \hline \hline \end{tabular}
\end{table}