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Nowadays, due to the SARS-CoV-2 pandemic, epidemic modelling is experiencinga constantly growing interest from researchers of heterogeneous fields ofstudy. Indeed, the vast literature on computational epidemiology offers solidgrounds for analytical studies and the definition of novel models aimed at bothpredictive and prescriptive scenario descriptions. To ease the access todiffusion modelling, several programming libraries and tools have been proposedduring the last decade: however, to the best of our knowledge, none of them isexplicitly designed to allow its users to integrate public interventions intheir model. In this work, we introduce UTLDR, a framework that can simulatethe effects of several public interventions (and their combinations) on theunfolding of epidemic processes. UTLDR enables the design of compartmentalmodels incrementally and to simulate them over complex interaction networktopologies. Moreover, it allows integrating external information on theanalyzed population (e.g., age, gender, geographical allocation, and mobilitypatterns\dots) and to use it to stratify and refine the designed model. Afterintroducing the framework, we provide a few case studies to underline itsflexibility and expressive power.

UTLDR: an agent-based framework for modeling infectious diseases and public interventions