## 1 Introduction  
On 11 March 2020, the World Health Organization declared COVID-19, the disease generated by the newly discovered SARS-CoV-2 virus, a pandemic [1]. Almost three years later, confirmed cases around the world reached 700 million, with about 7 million official casualties [2]. The unprecedented velocity and dimension of such a pandemic posed significant threats to governments worldwide at many levels, specifically with regards to healthcare systems and their ability to promptly react to such phenomena. In this context, technological solutions can provide support to public health policy-makers: among the various fields, modeling of pandemic spreading is one of the key tools [3; 4]. The possibility to forecast the pandemic evolution at different scales (both in time and space) represents a crucial asset in the management of available resources at different levels of healthcare delivery [4; 5]. To this aim, traditional modeling approaches in epidemiology are based on mathematical equations systems [4]: a comprehensive review of such solutions can be found in [6]. However, in this review study, it is also evidenced that this traditional approach has several limitations: mathematical models are based on strong assumptions, such as variables (e.g., transmission rates) considered constant and uniform, while in the real world, they change over time and space; moreover, these models stand valid only for a specific epidemic phase. As a consequence, they struggle to perform with the emergence of new data, and hardly pass the external validity criteria [6]. COVID-19, however, represents the first pandemic in the 'AI' (Artificial Intelligence) era: this new approach can offer a valuable alternative to traditional modeling, thus becoming a game-changer asset, as indicated by the recent scientific literature [4; 5; 7] and as witnessed by the large amount of research work conducted on this topic [7; 8; 9; 10; 11; 12; 13; 14], even prior to COVID-19 and with general reference to infectious diseases [15], so that the opportunity of implementing AI solutions, especially machine learning (ML) techniques, as a support for public health authorities [5; 6; 7; 16], clearly emerges. Based on what the literature reported [3; 4; 5; 6; 7], several challenges in predictive models for COVID-19 are still unsolved [3; 7], and could be categorized into four topics:  
Data reliability:  
Until the end of 2020, available data were not enough to train models [7], both in terms of quantity and quality. Specifically, official diagnoses are considered a troublesome data source [13; 17; 18; 19; 20], partly due to the natural development of the phenomenon, but also exacerbated by the struggle of data-flow infrastructures [3], due to both technological and regulatory obstacles. In particular, hospitals and testing laboratories are bound by logistics and operational issues, resulting in a bottleneck for the data-driven processes [16] and also generating frequent and irregular artificial spikes and drops in records [3], a problem that still persists and hinders the implementation of predictive models. Focus on the spatial component:  
Geographical and environmental conditions differ greatly among countries, but also within countries and regions, generating different epidemiologic scenarios [16], making it difficult to perform a comparison between different locations [3]. Therefore, a strong spatial perspective is needed [7]. While the focus is usually on the numerical estimation of aggregated indicators (such as infected cases, recovered, etc. ), more attention should be directed towards the identification of the most vulnerable communities and territorial areas [21]. To this scope, the use of GIS (Geographic Information Systems) is highly recommended from the literature [16]. Models granularity:  
In light of the proposed spatial perspective, conditions differ significantly among countries and regions, thus requiring an analysis at the level of municipalities [16].