  Nowadays, due to the SARS-CoV-2 pandemic, epidemic modelling is experiencing
a constantly growing interest from researchers of heterogeneous fields of
study. Indeed, the vast literature on computational epidemiology offers solid
grounds for analytical studies and the definition of novel models aimed at both
predictive and prescriptive scenario descriptions. To ease the access to
diffusion modelling, several programming libraries and tools have been proposed
during the last decade: however, to the best of our knowledge, none of them is
explicitly designed to allow its users to integrate public interventions in
their model. In this work, we introduce UTLDR, a framework that can simulate
the effects of several public interventions (and their combinations) on the
unfolding of epidemic processes. UTLDR enables the design of compartmental
models incrementally and to simulate them over complex interaction network
topologies. Moreover, it allows integrating external information on the
analyzed population (e.g., age, gender, geographical allocation, and mobility
patterns\dots) and to use it to stratify and refine the designed model. After
introducing the framework, we provide a few case studies to underline its
flexibility and expressive power.
