Disease Simulation in Airport Scenario Based on Individual Mobility Model

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