Artificial Intelligence Model of Drive-Through Vaccination Simulation
1
Disaster & Emergency Management, School of Administrative Studies, York University, Toronto, ON M3J 1P3, Canada
2
Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM), York University, Toronto, ON M3J 1P3, Canada
3
Department of Mathematics and Statistics and Laboratory for Industrial and Applied Mathematics, York University, Toronto, ON M3J 1P3, Canada
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2021, 18(1), 268; https://doi.org/10.3390/ijerph18010268
Received: 16 October 2020 / Revised: 21 December 2020 / Accepted: 23 December 2020 / Published: 31 December 2020
(This article belongs to the Special Issue Big Data and Mathematical Modeling in Biomedicine)
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.
View Full-Text
Keywords:
COVID-19 pandemic; artificial intelligence; drive-through; mass vaccination; discrete event simulation
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Asgary, A.; Valtchev, S.Z.; Chen, M.; Najafabadi, M.M.; Wu, J. Artificial Intelligence Model of Drive-Through Vaccination Simulation. Int. J. Environ. Res. Public Health 2021, 18, 268. https://doi.org/10.3390/ijerph18010268
AMA Style
Asgary A, Valtchev SZ, Chen M, Najafabadi MM, Wu J. Artificial Intelligence Model of Drive-Through Vaccination Simulation. International Journal of Environmental Research and Public Health. 2021; 18(1):268. https://doi.org/10.3390/ijerph18010268
Chicago/Turabian StyleAsgary, Ali; Valtchev, Svetozar Z.; Chen, Michael; Najafabadi, Mahdi M.; Wu, Jianhong. 2021. "Artificial Intelligence Model of Drive-Through Vaccination Simulation" Int. J. Environ. Res. Public Health 18, no. 1: 268. https://doi.org/10.3390/ijerph18010268
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
Search more from Scilit
