ABSTRACT
Agent-based modeling and simulation techniques are widely and successfully used for analyzing complex and emergent phenomena in many research and application areas. Among the many different reasons which sustain the flexibility and success of such techniques, it is important to mention the availability of a great variety of software tools, easing (1) the development of models, (2) the execution of simulations, and (3) the analysis of results. Currently, with the rapid global spread of the COVID-19 pandemic, one of the most important research area is dedicated to define algorithms and systems to support epidemic forecasting simulations, scalable on large populations. In particular, in this paper, we propose an agent-based epidemic model and a distributed architecture that can be used for the simulation of populations represented by millions of agents. Moreover, the paper presents the results of the simulations on the data of the population of Lombardy. © 2021 CEUR-WS. All rights reserved.
ABSTRACT
Agent-based modeling and simulation are some powerful techniques that are widely used with success for analyzing complex and emergent phenomena in many research and application areas. Many different reasons are behind the success of such techniques, among which an important mention goes to the availability of a great variety of software tools, that ease the development of models, as well as the execution of simulations and the analysis of results. This paper presents an actor software library, called ActoDeS, for the development of concurrent and distributed systems, and shows how it can be a suitable mean for building flexible and scalable epidemic forecasting simulations. In particular, the paper presents the first results of the experimentation of ActoDeS for defining a COVID-19 epidemic diffusion model and for supporting the simulation in large populations. © 2020 Copyright for this paper by its authors.