## 1 Introduction
This micro-scale is needed to be effective in supporting decision making, and it would be expected by both public health authorities and public opinion [16]. However, most of the models do not reach this level of granularity, mainly due to limited data availability [16], also hindering the possibility of comparative analyses across different locations [3]. For this reason, researchers suggest to 'Prioritize the collection of data with high resolution in space and time' [3], allowing the development of high-granularity models. Real-world application:  
The context of a pandemic emergency generally requires an immediate and direct approach, leaving no space for the development of new models [16], especially when their outcome is strictly technical and expressed in statistical terms [3]. As a consequence, the vast majority of models are considered not ready for an application in the real-world [7], and their adoption could be very limited and slow [5]. To tackle this issue, the scientific community is invited to 'consider more interpretable targets and ways to express uncertainty', as well as 'communicating the range of plausible outcomes instead of point predictors only' [3]. With the aim of tackling these aspects, our aim is to propose a predictive model for the monitoring of COVID-19, with a specific focus on the frontline healthcare battlefield, as represented by the Emergency Medical Services (EMS), in charge of out-of-hospital first care in the territory. The key role of EMS in the fight against COVID-19 was acknowledged by the World Health Organization (WHO) [22] and assessed in the scientific literature [23; 24]. A peak in the demand for EMS during the pandemic (especially in the first phases) was recorded across the world, such as in the USA [25; 26; 27], UK [28], Denmark [29], France [30], Spain [31], Turkey [32], India [33], Japan [34], and Saudi Arabia [35], as well as in the target area of this study, Lombardy region in Italy [36; 37; 38]. Depending on the timing of the outbreak and of the consequent restrictive measures, EMS were, in some cases, put under a level of stress that made it necessary to re-arrange protocols to face the pandemic [39; 40; 41]. EMS is considered a reasonable target for prediction models [25], especially for the need of an optimized allocation and use of resources [42], but it is also a very valuable source of data relating to the pandemic of COVID-19 [21; 33; 35; 43; 44], as well as for other influenza-like syndromes [45]. The first immediate application is in terms of territorial monitoring [21; 33; 36; 37], but a prediction task based on such data is also considered in the literature [7; 33; 42; 43; 44; 45; 46]. Accordingly, the model presented in this study considers EMS data as both the target output and the data source for the prediction task. The desired output is therefore a predictive model providing information (for each municipality in the target territory) about the position to be expected in the following weeks with respect to the epidemic curve: increasing phase, peak/plateau phase, decreasing phase. To this scope, the number of vehicles dispatched and the calls received by the EMS department was considered, applying a machine learning model with a supervised learning approach in an attempt to predict the future trend in the curve. While this study builds upon the same Emergency Medical Services (EMS) data structure used in previous research [36; 37; 38], it introduces a distinct and novel modeling framework. Our earlier works focused, respectively, on validating EMS data as a proxy for epidemic activity [36] and on developing a real-time early alert system for territorial monitoring [38]. In contrast, the present study advances a predictive model targeting weekly epidemic scenarios, newly defined for this purpose. It employs a dual-labeling machine learning strategy with full performance evaluation via cross-validation and independent testing.