Disease Simulation in Airport Scenario Based on Individual Mobility Model

Abstract

As the rapid-spreading disease COVID-19 occupies the world, most governments adopt strict control policies to alleviate the impact of the virus. These policies successfully reduced the prevalence and delayed the epidemic peak, while they are also associated with high economic and social costs. To bridge the microscopic epidemic transmission patterns and control policies, simulation systems play an important role. In this work, we propose an agent-based disease simulator for indoor public spaces, which contribute to most of the transmission in cities. As an example, we study Guangzhou Baiyun International Airport, which is one of the most bustling aviation hubs in China. Specifically, we design a high-efficiency mobility generation module to reconstruct the individual trajectories considering both lingering behavior and crowd mobility, which greatly enhances the credibility of the simulated mobility and ensures real-time performance. Based on the individual trajectories, we propose a multi-path disease transmission module optimized for indoor public spaces, which includes three main transmission paths as close contact transmission, aerosol transmission, and object surface transmission. We design a novel convolution-based algorithm to mimic the diffusion process, which can leverage the high concurrent capability of the graphics processing unit to accelerate the simulation process. Leveraging our simulation paradigm, the effectiveness of common policy interventions can be quantitatively evaluated. For mobility interventions, we find that lingering control is the most effective mobility intervention with 32.35% fewer infections, while increasing social distance and increasing walking speed have a similar effect with 15.15% and 18.02% fewer infections. It demonstrates the importance of introducing crowd mobility into disease transmission simulation. For transmission processes, we find the aerosol transmission involves in 99.99% of transmission, which highlights the importance of ventilation in indoor public spaces. Our simulation also demonstrates that without strict entrance detection to identify the input infections, only performing frequent disinfection cannot achieve desirable epidemic outcomes. Based on our simulation paradigm, we can shed light on better policy designs that achieve a good balance between disease spreading control and social costs.