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10.3390/ijerph18010268 http://scihub22266oqcxt.onion/10.3390/ijerph18010268 33396526!7796369!33396526     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Environ+Res+Public+Health 2020 ; 18 (1): ä Nephropedia Template TP {{tp|p=33396526|t=2020. Artificial Intelligence Model of Drive-Through Vaccination Simulation |pdf=|usr=}}{{33396526}} gab.com Text Artificial Intelligence Model of Drive-Through Vaccination Simulation JO: Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=33396526 #FOAvip 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. Twit Text FOAVip Artificial Intelligence Model of Drive-Through Vaccination Simulation ????Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=33396526 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33396526 #FOAvip English Wikipedia <ref>{{Cite pmid|33396526}}</ref> Artificial Intelligence Model of Drive-Through Vaccination Simulation #MMPMID33396526 Asgary A; Valtchev SZ; Chen M; Najafabadi MM; Wu J Int J Environ Res Public Health 2020[Dec]; 18 (1): ä PMID33396526show ga 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. |*Artificial Intelligence[MESH] |COVID-19 Vaccines/administration & dosage[MESH] |COVID-19/prevention & control[MESH] |Humans[MESH] |Mass Vaccination/*organization & administration[MESH]Artificial Intelligence Model of Drive-Through Vaccination Simulation (epmc).pdf DeepDyve Pubget Overpricing