Agent-Based Simulation for Infectious Disease Modelling over a Period of Multiple Days, with Application to an Airport Scenario

Abstract

With the COVID-19 pandemic, the role of infectious disease spreading in public places has been brought into focus more than ever. Places that are of particular interest regarding the spread of infectious diseases are international airport terminals, not only for the protection of staff and ground crew members but also to help minimize the risk of the spread of infectious entities such as COVID-19 around the globe. Computational modelling and simulation can help in understanding and predicting the spreading of infectious diseases in any such scenario. In this paper, we propose a model, which combines a simulation of high geometric detail regarding virus spreading with an account of the temporal progress of infection dynamics. We, thus, introduce an agent-based social force model for tracking the spread of infectious diseases by modelling aerosol traces and concentration of virus load in the air. We complement this agent-based model to have consistency over a period of several days. We then apply this model to investigate simulations in a realistic airport setting with multiple virus variants of varying contagiousness. According to our experiments, a virus variant has to be at least twelve times more contagious than the respective control to result in a level of infection of more than 30%. Combinations of agent-based models with temporal components can be valuable tools in an attempt to assess the risk of infection attributable to a particular virus and its variants.

Maritime transportation and people mobility in the early diffusion of COVID-19 in Croatia

“…Other methods were previously employed to study spreading of infection diseases through interaction between different geographical regions. It is worth mentioning the different methods among which lattice simulation using SIR models with space extension and interaction using Bayesian maximum entropy theory ( 38 ), lattice spatio-temporal modeling framework integrating SIR and log-Gaussian Cox process (LGCP) process ( 39 ), graph modelization where policies to curb the spreading are tested by removing individuals ( 40 ) or agent-based simulations of aerosol and pedestrian trails to track the spreading at the level of an airport ( 41 ). The eRG method is more economical as it only considers the total number of infections, and it has been used in conjunction with airborne traffic to study the early diffusion of COVID-19 in the United States ( 42 ).…”