Exploring a targeted approach for public health capacity restrictions during COVID-19 using a new computational model

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

doi:10.1016/j.idm.2024.01.001

Exploring a targeted approach for public health capacity restrictions during COVID-19 using a new computational model

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

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	234-244
Number of pages	11
Journal	Infectious Disease Modelling
Volume	9
Issue number	1
DOIs	https://doi.org/10.1016/j.idm.2024.01.001
Publication status	Published - Mar 2024

Keywords

Agent-based epidemic modelling
Basic reproductive number
COVID-19
Monte-carlo
Public health
Small business capacity restrictions

ASJC Scopus subject areas

Health Policy
Infectious Diseases
Applied Mathematics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.idm.2024.01.001

Cite this

Micuda, A. N., Anderson, M. R., Babayan, I., Bolger, E., Cantin, L., Groth, G., Pressman-Cyna, R., Reed, C. Z., Rowe, N. J., Shafiee, M., Tam, B., Vidal, M. C., Ye, T., & Martin, R. D. (2024). Exploring a targeted approach for public health capacity restrictions during COVID-19 using a new computational model. Infectious Disease Modelling, 9(1), 234-244. https://doi.org/10.1016/j.idm.2024.01.001

Micuda, AN, Anderson, MR, Babayan, I, Bolger, E, Cantin, L, Groth, G, Pressman-Cyna, R, Reed, CZ, Rowe, NJ, Shafiee, M, Tam, B, Vidal, MC, Ye, T & Martin, RD 2024, 'Exploring a targeted approach for public health capacity restrictions during COVID-19 using a new computational model', Infectious Disease Modelling, vol. 9, no. 1, pp. 234-244. https://doi.org/10.1016/j.idm.2024.01.001

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abstract = "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.",

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Exploring a targeted approach for public health capacity restrictions during COVID-19 using a new computational model

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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