Covid-19 Pandemic Infection Detection in a Confined Space Using An Agent- Based Model

Abstract

As the world observes a new pandemic with COVID-19, it is clear that pathogens can spread rapidly and without recognition of borders. Outbreaks will continue to occur, and so the diseases’ transmission method must be thoroughly understood in order to minimize their impact. Some infections, such as influenza, tuberculosis and measles, known to spread through droplets in the air. In a confined space, the concentration can grow as more droplets released. This study examined a simulated confined space modelled as a hospital waiting area, where people who could have underlying conditions congregate and mix with potentially infectious individuals. It further investigated the impact of the volume of the waiting area, the number of people in the room, the placement of them as well as their weight. The simulation is an agent-based model (ABM), a computational model with the purpose of analyzing a system through the actions and cumulative consequences of autonomous agents. The presented ABM features embodied agents with differing body weights that can move, breathe and cough in a ventilated room. An investigation into current epidemiological models lead to the hypothesis that one may implement as a corresponding ABM, where it could possibly, also be improved upon. In this paper, it has shown that, all parameters of the Gammaitoni and Nucci model, been taken into account, in an ABM via the MASON library. In addition, proof produced, suggested that some flaws of the epidemiological model, should be amended in the ABM. It was demonstrated, the constructed model, account for proximity between susceptible people and infectors, an expressed limitation of the original model. KEYWORDS: transmission method, confined space, agent-based model, epidemiological models, Pandemic