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Artificial Intelligence Model of Drive-Through Vaccination Simulation

Author

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  • Ali Asgary

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

  • Svetozar Zarko Valtchev

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

  • Michael Chen

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

  • Mahdi M. Najafabadi

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

  • Jianhong Wu

    (Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM), York University, Toronto, ON M3J 1P3, Canada
    Department of Mathematics and Statistics and Laboratory for Industrial and Applied Mathematics, York University, Toronto, ON M3J 1P3, Canada)

Abstract

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.

Suggested Citation

  • Ali Asgary & Svetozar Zarko Valtchev & Michael Chen & Mahdi M. Najafabadi & Jianhong Wu, 2020. "Artificial Intelligence Model of Drive-Through Vaccination Simulation," IJERPH, MDPI, vol. 18(1), pages 1-10, December.
    • Handle: RePEc:gam:jijerp:v:18:y:2020:i:1:p:268-:d:473408
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    References listed on IDEAS

    as
    1. Michael F Beeler & Dionne M Aleman & Michael W Carter, 2014. "A simulation case study to improve staffing decisions at mass immunization clinics for pandemic influenza," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(4), pages 497-511, April.
    2. Adrian Ramirez-Nafarrate & Joshua D. Lyon & John W. Fowler & Ozgur M. Araz, 2015. "Point-of-Dispensing Location and Capacity Optimization via a Decision Support System," Production and Operations Management, Production and Operations Management Society, vol. 24(8), pages 1311-1328, August.
    3. Eva K. Lee & Ferdinand Pietz & Bernard Benecke & Jacquelyn Mason & Greg Burel, 2013. "Advancing Public Health and Medical Preparedness with Operations Research," Interfaces, INFORMS, vol. 43(1), pages 79-98, February.
    4. Nathaniel Hupert & Alvin I. Mushlin & Mark A. Callahan, 2002. "Modeling the Public Health Response to Bioterrorism: Using Discrete Event Simulation to Design Antibiotic Distribution Centers," Medical Decision Making, , vol. 22(1_suppl), pages 17-25, September.
    5. Fares Qeadan & Trenton Honda & Lisa H. Gren & Jennifer Dailey-Provost & L. Scott Benson & James A. VanDerslice & Christina A. Porucznik & A. Blake Waters & Steven Lacey & Kimberley Shoaf, 2020. "Naive Forecast for COVID-19 in Utah Based on the South Korea and Italy Models-the Fluctuation between Two Extremes," IJERPH, MDPI, vol. 17(8), pages 1-14, April.
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    Cited by:

    1. Davide Barbieri & Enrico Giuliani & Anna Del Prete & Amanda Losi & Matteo Villani & Alberto Barbieri, 2021. "How Artificial Intelligence and New Technologies Can Help the Management of the COVID-19 Pandemic," IJERPH, MDPI, vol. 18(14), pages 1-10, July.

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