## 4 Discussion  
In the proposed model, the different issues identified in the literature were all addressed, providing point-by-point solutions and assessing their efficacy:  
Data reliability: the problems pointed out by the scientific literature can be tackled considering EMS-generated data rather than official diagnosis, as stated in [21; 47]. These data are unbiased by hospitals and laboratories procedures, as they are directly generated by citizens, are collected quickly, and are simple, standardized, and available in the majority of EMS systems. Precedent studies conducted by our group [36; 37; 38] already demonstrated  
Figure 3: Map representation of a classification algorithm output, representing the prediction of spreading of COVID-19 as assigned by post-processing a machine learning (see text for details) on ‘dynamic’ districts centered in each municipality in the territory of Lombardy region, Italy, on the basis of vehicles dispatched by the EMS department for respiratory and infective issues. Four different dates are considered: (**A**) 15 March 2020 (peak of first wave), (**B**) 5 November 2020 (peak of second wave), (**C**) 15 March 2021 (peak of third wave), and (**D**) 1 December 2021 (latest developments in the dataset). how these data, despite not being official diagnoses, can be strictly related to the pattern of the pandemic, as also confirmed in [48; 49; 50]. Focus on the spatial component: the proposed model is based on data that are strictly geo-referenced through geographic coordinates, and is spatially explicit. Rather than estimating the overall trend of the pandemic, the proposed model targets territorial monitoring, giving a likelihood of a short-term prediction about the condition of unit areas. This is obtained by combining GIS computational methodology with a data-science approach. Models granularity: the proposed model takes advantage of a spatial filtering, weighting all the events occurring in a statistically meaningful pool area according to their drive-time distance from the target point. With this approach, it was possible to obtain a model working at the geographic level of single municipalities, an approach already discussed and validated in a previous publication [38] and, to the best of our knowledge, not elsewhere proposed in the literature. Real-world application: the proposed model was developed in collaboration with the provider of EMS in Lombardy region (AREU, Agenzia Regionale Emergenza/Urgenza). Therefore, the development involved the main stakeholder during all the implementation phases, starting from the very first conceptualization; hence, giving priority to its applicability, in terms of data requirements, velocity, and interpretability. In order to make it more comprehensible, the problem was faced as a classification task, pre-defining different scenarios of EMS requests as the target output, as suggested in [3]. To effectively handle uncertainty, a post-processing phase was applied in order to provide, in output, a primary and a secondary-likely scenario. While many foundational elements to address key challenges were established in our previous works (particularly regarding the use of EMS data [36], spatial filtering, and stakeholder co-design [38]), those studies focused on descriptive or near real-time monitoring. In contrast, the present work tackles the distinct task of short-term prediction, which entails additional methodological complexities. Adapting the framework to this forecasting context was not a trivial extension, but a necessary advancement, demonstrating that the same core principles can be effectively applied to support proactive epidemic management. The developed model does not simply provide a level of confidence about the spatial spreading of the disease (as achieved through a precedent analytical framework [38]), but also allows predictions on the expected trend for the following week in terms of incidence of medical vehicles dispatched by EMS for respiratory and infective issues.