Investigate Respiratory Infectious disease transmission in indoor situation by spatially explicit ABM

Keywords: Virus transmission, Agent-Based model, Machine Learning
Session Type: Virtual Paper Abstract
Day: Tuesday
Session Start / End Time: 3/1/2022 11:20 AM (Eastern Time (US & Canada)) - 3/1/2022 12:40 PM (Eastern Time (US & Canada))
Room: Virtual 21

Abstract

The Centers for Disease Control and Prevention CDC announced guidelines regarding face masks and social distancing to stop the spread of COVID-19 . However, simple 6 feet social distancing without considering spatial structures and individual respiratory behaviors such as speaking, coughing, or sneezing could cause unexpected transmission of disease in indoor spaces. Transmission of respiratory disease such as COVID-19 mainly occurs in indoor environments, which raises the need to investigate indoor virus spreading patterns to inform policies aimed at reducing infections without relying on the simple 6-feet social distancing rule. To address the issue, investigating the relationship between respiratory behavior as well as dynamic movement of people and virus inhalation amount in indoor spaces is essential for forming guidelines for curbing the transmission of the disease.
In this study, first, we used spatially explicit Agent-based modelling to find out how potential infection risk (encounters between people) is affected by different configurations of indoor space. Second, we investigate the linkage between parameters including respiratory behaviors (breath, speak, cough, and sneeze) and spatially dynamic movement of people to virus inhalation amount. The results show us how we need to move and behavior to curb the virus inhalation amount in indoor space.