## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & & & & & & & & \\ \cline{6-10}
**LAB** & \(n\) & **1** & **2** & **3** & **4** & **5** & **1** & **2** & **3** & **4** & **5** \\ \hline
11 & 25,533 & 28,268 & 71 & **0** & **0** & 2 & 31.176 & 0.28\% & 0.00\% & 0.00\% & 0.01\% \\
12 & 30,183 & 26530 & 992 & **16** & **36** & 28 & 38.976 & 3.29\% & 0.05\% & 0.12\% & 0.09\% \\ \hline
21 & 12,379 & 5607 & 1259 & 73 & 52 & 49 & 7.616 & 14.218 & 0.59\% & 0.42\% & 0.40\% \\
22 & 240 & 97 & 25 & **0** & 0 & 3 & 40.42\% & 8.97\% & 0.00\% & 0.00\% & 1.25\% \\
25 & 4961 & 2183 & 1585 & 465 & 317 & 36 & 44.00\% & 42.65\% & 9.37\% & 6.39\% & 6.17\% \\ \hline
33 & 2 & **0** & **0** & **2** & 0 & **0** & 0.00\% & 0.00\% & 10.00\% & 0.00\% & 0.00\% \\
34 & 0 & **0** & **0** & **0** & **0** & 0 & NA & NA & NA & NA & NA \\
35 & 3533 & 118 & 154 & 1752 & 855 & 479 & 3.34\% & 4.36\% & 49.06\% & 24.20\% & 13.35\% \\ \hline
43 & 709 & 20 & 20 & 273 & 200 & 128 & **2.82\%** & 2.82\% & 39.21\% & 31.06\% & 18.05\% \\
44 & 0 & **0** & **0** & 0 & 0 & 0 & NA & NA & NA & NA & NA \\
45 & 1173 & 114 & 45 & 334 & 31.91 & 316 & 9.72\% & 3.84\% & 28.47\% & 26.8\% & 26.94\% \\ \hline
53 & 3037 & 202 & 121 & 676 & 740 & 1180 & 6.65\% & 3.98\% & 22.66\% & 24.37\% & 58.85\% \\
54 & 0 & **0** & 0 & 0 & 0 & 0 & NA & NA & NA & NA & NA \\
55 & 6 & **0** & **0** & 1 & 1 & 4 & **0.00\%** & 0.00\% & 16.67\% & 16.67\% & 66.69\% \\ \hline  & & & & & & & & & & & & \\ \hline  & & & & & & & & & & & & \\ \hline  & & & & & & & & & & & & \\ \end{tabular}
\end{table} 
Table 4: Results of an external validation for a classification of ‘dynamic’ territorial districts (centered on each municipality, see text for details) 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, with the training set spanning from 1 January 2020 to 30 September 2021, and the test set from 1 October 2021 to 1 December 2021. Five scenarios are identified: ‘1’ = no diffusion, ‘2’ = no diffusion with oscillations, ‘3’ = active spreading with decreasing curve, ‘4’ = active spreading with stable curve, ‘5’ = active spreading with increasing curve. A double label (LAB), representing the first and second most likely scenarios, is assigned: a random forest algorithm on historical data (relevant to vehicles dispatched and calls received by the EMS department) provides the first label, while a post-processing based on the distributions of assignment probabilities computes the second label (see text for details). The quality of predictions is color-coded (cells background color): dark blue = prediction level A: the scenario indicated by the first label occurred, light-blue = prediction level B: the scenario indicated by the second label occurred, lighter grey = prediction level C: neither of the two scenarios indicated occurred, but the ‘adjacent’ one occurred, dark grey = prediction level D: the scenario occurred is completely different from the one predicted. In the upper table, the first column represents the label assigned, the second column reports the total number of records with that assignment, and in the following columns, the number (left) and ratio (right) of different occurred scenarios (true label) is reported. In the lower table, the ratio of the different prediction classes is reported, for all records (first column), for ‘no diffusion’ scenarios only (second column), and for ‘active spread’ scenarios only (third column). During the test phase, the predictive model correctly identified the epidemic scenario with the first label in 69.5% of cases and capturing 84.9% within the top two predicted labels. Performance was especially strong in no diffusion scenarios, with 79.5% of cases accurately classified with the first label and 95.3% within the double-label predictions.