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Modelling COVID-19 transmission in supermarkets using an agent-based model.

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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.

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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.