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

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