Fine-grained parallel social modelling for analyzing COVID-19 propagation

Abstract

Agent-based epidemiological simulators have been proven to be one of the most successful tools for the analysis of the COVID-19 propagation. The ability of these tools to reproduce the behavior and interactions of each single individual leads to accurate and detailed results, that can be used to model fine-grained health-related policies like selective vaccination campaigns or immunity waning. One characteristic of these tools is the large amount of input data and computational resources and that they require. This relies on the development of parallel algorithms and methodologies for generating, accessing and processing large volumes of data from multiple data sources. This work presents a parallel workflow for extending the social modelling of EpiGraph, an agent-based simulator. We have included two novel parallel social generation stages -that provide detailed and realistic social model- and one new visualization stage. The work presents a description of the algorithms used in each stage and a practical evaluation on a real platform. Results show that this contribution can be efficiently executed in parallel architectures and increases the simulation detail level, representing a significant advance in the simulator scenario modelling.