Modelling COVID-19 transmission in supermarkets using an agent-based model
1Quantitative Research, G-Research, London, United Kingdom.
Plos One
|April 9, 2021
Summary
This study introduces an agent-based model to assess COVID-19 mitigation strategies in supermarkets. The model helps estimate virus exposure time and infections, guiding retailers to implement effective policies for customer and staff safety.
Area of Science:
- Epidemiology
- Computational modeling
- Public health
Background:
- Supermarkets implemented various COVID-19 policies to reduce virus transmission.
- Policies included customer limits, layout changes, and mandatory face coverings.
Purpose of the Study:
- To quantitatively assess the effectiveness of supermarket COVID-19 mitigation strategies.
- To develop an agent-based model for simulating virus transmission in retail environments.
Main Methods:
- Formulated an agent-based model of customer movement in a supermarket network.
- Incorporated a virus transmission model based on customer proximity and exposure time.
- Applied the model to synthetic store and shopping data.
Main Results:
- The model can estimate customer exposure time and potential infections.
- Demonstrated the ability to simulate and evaluate different store interventions.
- Provided a framework for retailers to optimize policies for virus transmission reduction.
Conclusions:
- The agent-based model offers a valuable tool for supermarkets to assess and implement effective COVID-19 safety policies.
- The model can help reduce virus transmission, protecting both customers and staff.
- Open-source code is available for broader adoption and refinement.
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