A Drive-through Simulation Tool for Mass Vaccination during COVID-19 Pandemic.

COVID-19 agent-based simulation discrete event simulation drive-through mass immunization mass vaccination point of dispensing

Journal

Healthcare (Basel, Switzerland)
ISSN: 2227-9032
Titre abrégé: Healthcare (Basel)
Pays: Switzerland
ID NLM: 101666525

Informations de publication

Date de publication:
09 Nov 2020
Historique:
received: 05 10 2020
revised: 29 10 2020
accepted: 02 11 2020
entrez: 13 11 2020
pubmed: 14 11 2020
medline: 14 11 2020
Statut: epublish

Résumé

Several research and development teams around the world are working towards COVID-19 vaccines. As vaccines are expected to be developed and produced, preparedness and planning for mass vaccination and immunization will become an important aspect of the pandemic management. Mass vaccination has been used by public health agencies in the past and is being proposed as a viable option for COVID-19 immunization. To be able to rapidly and safely immunize a large number of people against SARS-CoV-2, different mass vaccination options are available. Drive-through facilities have been successfully used in the past for immunization against other diseases and for testing during COVID-19. In this paper we introduce a drive-through vaccination simulation tool that can be used to enhance the planning, design, operation, and feasibility and effectiveness assessment of such facilities. The simulation tool is a hybrid model that integrates discrete event and agent-based modeling techniques. The simulation outputs visually and numerically show the average processing and waiting times and the number of cars and people that can be served (throughput values) under different numbers of staff, service lanes, screening, registration, immunization, and recovery times.

Identifiants

pubmed: 33182336
pii: healthcare8040469
doi: 10.3390/healthcare8040469
pmc: PMC7711491
pii:
doi:

Types de publication

Journal Article

Langues

eng

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Auteurs

Ali Asgary (A)

Disaster & Emergency Management, School of Administrative Studies and Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM), York University, Toronto, ON M3J 1P3, Canada.

Mahdi M Najafabadi (MM)

Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM), York University, Toronto, ON M3J 1P3, Canada.

Richard Karsseboom (R)

Duty Officer, Departmental Emergency Operations Centre, Community and Health Services, The Regional Municipality of York, Newmarket, ON L3Y 4W5, Canada.

Jianhong Wu (J)

Department of Mathematics and Statistics and Laboratory for Industrial and Applied Mathematics, York University, Toronto, ON M3J 1P3, Canada.

Classifications MeSH