2020
DOI: 10.3390/ijerph18010268
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Artificial Intelligence Model of Drive-Through Vaccination Simulation

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 f… Show more

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Cited by 34 publications
(24 citation statements)
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“…ML and AI can contribute also to the distribution of the vaccine and to monitorvaccinated people. For example, Asgary et al developed an ML model (turned to an online application) that can help mass vaccination planners to quickly monitor different types of drive-through mass vaccination facilities [ 40 ], while Bubar et al used a mathematical model to compare age and serostatus-stratified prioritization strategies [ 41 ]. Furthermore, Mathieu et al have realized a COVID-19 vaccination dataset, a regularly updated global public dataset that tracks the scale and rate of the vaccine rollout across the world [ 42 ].…”
Section: Resultsmentioning
confidence: 99%
“…ML and AI can contribute also to the distribution of the vaccine and to monitorvaccinated people. For example, Asgary et al developed an ML model (turned to an online application) that can help mass vaccination planners to quickly monitor different types of drive-through mass vaccination facilities [ 40 ], while Bubar et al used a mathematical model to compare age and serostatus-stratified prioritization strategies [ 41 ]. Furthermore, Mathieu et al have realized a COVID-19 vaccination dataset, a regularly updated global public dataset that tracks the scale and rate of the vaccine rollout across the world [ 42 ].…”
Section: Resultsmentioning
confidence: 99%
“…Simulations are established as important methods to understand future scenarios in light of current conditions [3,36]. As a practice and a methodology, modeling lays the ground on necessary assumptions and parameters according to which agents are expected to behave and under which systems operate [37].…”
Section: Simulation Modeling Option and Artificial Intelligencementioning
confidence: 99%
“…In face of known data and modeling constraints concerning the use of data-intensive applications [25,35], steps are being taken incrementally, ranging from the use of unsupervised methods towards a panoramic view of pandemic's behavior [62,63], to more simulation-centered ones, focused on parameter optimization and predictions [3,31]. Testing in school settings carries some of operational obstacles, including discomfort for children, the logistics of the delivery, and public acceptance [4,61].…”
Section: Ai In Simulation Modeling For School Testing Scenariosmentioning
confidence: 99%
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