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

TL;DR

UTLDR is an agent-based framework that models infectious disease spread and public interventions, allowing detailed simulation of epidemic scenarios with demographic and mobility data integration.

Contribution

It introduces UTLDR, a novel framework enabling the integration of public interventions into epidemic models and supports complex network and demographic data incorporation.

Findings

UTLDR effectively simulates various public intervention strategies.

The framework demonstrates flexibility in modeling complex interaction networks.

Case studies show UTLDR's ability to refine epidemic predictions with demographic data.

Abstract

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.