Artificial Intelligence Model of Drive-Through Vaccination Simulation.
COVID-19 pandemic
artificial intelligence
discrete event simulation
drive-through
mass vaccination
Journal
International journal of environmental research and public health
ISSN: 1660-4601
Titre abrégé: Int J Environ Res Public Health
Pays: Switzerland
ID NLM: 101238455
Informations de publication
Date de publication:
31 12 2020
31 12 2020
Historique:
received:
16
10
2020
revised:
21
12
2020
accepted:
23
12
2020
entrez:
5
1
2021
pubmed:
6
1
2021
medline:
12
1
2021
Statut:
epublish
Résumé
Planning for mass vaccination against SARS-Cov-2 is ongoing in many countries considering that vaccine will be available for the general public in the near future. Rapid mass vaccination while a pandemic is ongoing requires the use of traditional and new temporary vaccination clinics. Use of drive-through has been suggested as one of the possible effective temporary mass vaccinations among other methods. In this study, we present a machine learning model that has been developed based on a big dataset derived from 125K runs of a drive-through mass vaccination simulation tool. The results show that the model is able to reasonably well predict the key outputs of the simulation tool. Therefore, the model has been turned to an online application that can help mass vaccination planners to assess the outcomes of different types of drive-through mass vaccination facilities much faster.
Identifiants
pubmed: 33396526
pii: ijerph18010268
doi: 10.3390/ijerph18010268
pmc: PMC7796369
pii:
doi:
Substances chimiques
COVID-19 Vaccines
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Ontario Research Fund
ID : xxx
Pays : International
Organisme : Public Health Agency of Canada
ID : yyy
Pays : International
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