Assessing the impact of architectural and behavioral interventions for controlling indoor COVID-19 infection risk: An agent-based approach

Abstract

The COVID-19 pandemic changed our lives, forcing us to reconsider our built environment. In some buildings with high traffic flow, infected individuals release viral particles during movement. The complex interactions between humans, building, and viruses make it difficult to predict indoor infection risk by traditional computational fluid dynamics methods. The paper developed a spatially-explicit agent-based model to simulate indoor respiratory pathogen transmission for buildings with frequent movement of people. The social force model simulating pedestrian movement and a simple forcing method simulating indoor airflow were coupled in an agent-based modeling environment. The impact of architectural and behavioral interventions on the indoor infection risk was then compared by simulating a supermarket case. We found that wearing a mask was the most effective single intervention, with all people wearing masks reducing the percentage of infections to 0.08%. Among the combined interventions, the combination of customer control is the most effective and can reduce the percentage of infections to 0.04%. In addition, the extremely strict combination of all the interventions makes the supermarket free of new infections during its 8-h operation. The approach can help architects, managers, or the government better understand the effect of nonpharmaceutical interventions to reduce the infection risk and improve the level of indoor safety.

Keywords: Agent-based model; Architecture design; Behavioral interventions; Buildings with high traffic flow; Indoor SARS-CoV-2 transmission; Supermarket.

Fig. 1

Scenarios of indoor infection risk assessment and the ABM modeling framework and approach.

Fig. 2

Diagram of social force model to simulate the indoor crowding phenomenon.

Fig. 3

Distribution of droplet sizes during expectoration events.

Fig. 4

Schematic diagram of spread angle and distance during coughing and non-coughing event.

Fig. 5

The volume and terminal speed of droplets of different sizes in the model.

Fig. 6

Simple forcing method for simulating indoor airflow by agent-based modeling.

Fig. 7

The underground supermarket at Tianjin University, China.

Fig. 8

The customer's activity flow and checkout selection strategies.

Fig. 9

Scenarios with different spatial layouts.

Fig. 10

Scenarios with different air-vent layouts.

Fig. 11

The real-time visualization of customers’ status and environmental contamination level of a simulation for the base scenario at the fifth minute. Crowding is easy to form in two local spaces (areas “A” and “B”) with high traffic near checkouts. The degree of air contamination between the infected individual and the return air vent is higher than in other directions, and the shelves near the entrance have been polluted to a high degree.

Fig. 12

The 8-h variations of customer exposure and environmental contamination of a simulation for the base scenario of the supermarket.

Fig. 13

Customers’ cumulative virus exposure (a) and percentage of probable infections (b) for different spatial layout scenarios.

Fig. 14

Customers’ cumulative virus exposure (a) and percentage of probable infections (b) for different air ventilation scenarios.

Fig. 15

Customers’ cumulative virus exposure (a) and percentage of probable infections (b) for various air-vent layouts under filter efficiency 0.4 and 1.0.

Fig. 16

Customers’ cumulative virus exposure (a) and percentage of probable infections (b) for different customer control scenarios.

Fig. 17

Customers’ cumulative virus exposure (a) and percentage of probable infections (b) for different checkout control scenarios.

Fig. 18

Customers’ cumulative virus exposure (a) and percentage of probable infections (b) for different sanitary measures.

Fig. 19

Customers’ cumulative virus exposure (a) and percentage of probable infections (b) for different combinations of measures.