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Exploring a targeted approach for public health capacity restrictions during COVID-19 using a new computational model
oleh: Ashley N. Micuda, Mark R. Anderson, Irina Babayan, Erin Bolger, Logan Cantin, Gillian Groth, Ry Pressman-Cyna, Charlotte Z. Reed, Noah J. Rowe, Mehdi Shafiee, Benjamin Tam, Marie C. Vidal, Tianai Ye, Ryan D. Martin
Format: | Article |
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Diterbitkan: | KeAi Communications Co., Ltd. 2024-03-01 |
Deskripsi
This work introduces the Queen's University Agent-Based Outbreak Outcome Model (QUABOOM). This tool is an agent-based Monte Carlo simulation for modelling epidemics and informing public health policy. We illustrate the use of the model by examining capacity restrictions during a lockdown. We find that public health measures should focus on the few locations where many people interact, such as grocery stores, rather than the many locations where few people interact, such as small businesses. We also discuss a case where the results of the simulation can be scaled to larger population sizes, thereby improving computational efficiency.