## 4 Discussion
The model is based on a random forest algorithm, classifying data into five scenarios: 1 no diffusion at all, 2 no diffusion with suspect oscillation, 3 active spreading with descending curve, 4 active spreading with stable curve, 5 active spreading with increasing curve. The overall performance of the algorithm may appear low, with the most performant prediction (A) at 69.48% in testing. However, two considerations should be taken into account:  
This is a multivariate classification algorithm, with five classes, therefore a random assignment does not have a 50% probability of being right, but only a probability of 20%. For example, a 40% figure of correct classifications means that the correct class has a probability of being assigned by the algorithm 25% more than all the other classes, whose average probability of assignment is (100-40%) / 4 = 15%; hence, the relative difference from wrong classes to the right one is +166% of assignment probability. On this basis, the obtained values should be interpreted more positively than they would if they were obtained for a binary classification problem. Worst-case prediction (D) only occurred in 3.96% of records in the test set. The target aim is very demanding: the phenomenon under analysis is extremely complex, with a non-negligible random component, and the performed attempt of prediction is definitely non-trivial. As a matter of fact, there is no gold-standard about the performance to expect from such a prediction attempt, as all the most diffused models are based on mathematical modeling which, although successful, do not have a comparable level of geographic granularity, and are therefore less valuable as support for decision-makers: mathematical models can only predict the development of the epidemiologic curve on large regions (e.g., the whole of Lombardy) and over large periods of time (many weeks). Noticeably, the test phase showed an overall higher performance compared to validation (+8.68% prediction A, from 60.8 to 69.48%, and +6.06% predictions A + B, from 78.9 to 84.96%), yet this is evidently due to the higher prevalence in the test set of 'no diffusion' scenarios; as a matter of fact, the performance is improved under this condition (+3.19% prediction A, from 76.33 to 79.52%, and +6.51% predictions A + B, from 88.8 to 95.31%), yet it is significantly lower in 'active diffusion' scenarios (\(-\)10.35% prediction A, from 50.44 to 40.09%, and \(-\)6.13% predictions A + B, from 70.1 to 63.97%). Nevertheless, this difference is mainly due to an increase in predictions of level C under 'active diffusion' scenarios, with +6.2% (from 26.82 to 33.02%), thus still resulting in a satisfying performance, further confirmed by the overall similar percentage of predictions of level D (\(-\)0.34% from 4.3 to 3.96%). In particular, the algorithm struggles (label-specific prediction of level D > 5%) with three specific conditions:  
Scenarios 1 mistakenly classified as '25' (foreseen oscillation in the probability + possible curve increase), representing 44% of all '25' classifications. This error can be considered as a 'false positive', which is acceptable under an assumption of'screening test' application. Scenarios 1 mistakenly classified as '45' (no diffusion classified as active diffusion with either stable or increasing curve), representing 9.72% of all '45' classifications. This is also a 'false positive' condition, so the considerations made for the previous case also apply. Scenarios 1 mistakenly classified as '53' (no diffusion classified as active diffusion with either increasing or decreasing curve), representing 6.65% of all '53' classifications. Again is a 'false positive' condition. Considering the above, it is possible to state that the model performance is mainly affected by 'false positives', while 'false negatives' (unforeseen curve growth: scenario 5 not indicated by any in the first or second label) only occurred in 8.42% of the cases (210 samples of out 2495 with scenario 5 as the real label, less than 1 out of 10).