Version 1
: Received: 21 June 2021 / Approved: 22 June 2021 / Online: 22 June 2021 (08:30:30 CEST)
How to cite:
Bandyopadhyay, S.; Dutta, S.; Mukherjee, U. Pharmacy Impact on Vaccination Progress Using Machine Learning Approach. Preprints2021, 2021060533. https://doi.org/10.20944/preprints202106.0533.v1
Bandyopadhyay, S.; Dutta, S.; Mukherjee, U. Pharmacy Impact on Vaccination Progress Using Machine Learning Approach. Preprints 2021, 2021060533. https://doi.org/10.20944/preprints202106.0533.v1
Bandyopadhyay, S.; Dutta, S.; Mukherjee, U. Pharmacy Impact on Vaccination Progress Using Machine Learning Approach. Preprints2021, 2021060533. https://doi.org/10.20944/preprints202106.0533.v1
APA Style
Bandyopadhyay, S., Dutta, S., & Mukherjee, U. (2021). Pharmacy Impact on Vaccination Progress Using Machine Learning Approach. Preprints. https://doi.org/10.20944/preprints202106.0533.v1
Chicago/Turabian Style
Bandyopadhyay, S., Shawni Dutta and Upasana Mukherjee. 2021 "Pharmacy Impact on Vaccination Progress Using Machine Learning Approach" Preprints. https://doi.org/10.20944/preprints202106.0533.v1
Abstract
The novel coronavirus disease (COVID-19) has created immense threats to public health on various levels around the globe. The unpredictable outbreak of this disease and the pandemic situation are causing severe depression, anxiety and other mental as physical health related problems among the human beings. To combat against this disease, vaccination is essential as it will boost the immune system of human beings while being in the contact with the infected people. The vaccination process is thus necessary to confront the outbreak of COVID-19. This deadly disease has put social, economic condition of the entire world into an enormous challenge. The worldwide vaccination progress should be tracked to identify how fast the entire economic as well as social life will be stabilized. The monitor ofthe vaccination progress, a machine learning based Regressor model is approached in this study. This tracking process has been applied on the data starting from 14th December, 2020 to 24th April, 2021. A couple of ensemble based machine learning Regressor models such as Random Forest, Extra Trees, Gradient Boosting, AdaBoost and Extreme Gradient Boosting are implemented and their predictive performance are compared. The comparative study reveals that the AdaBoostRegressor outperforms with minimized mean absolute error (MAE) of 9.968 and root mean squared error (RMSE) of 11.133.
Copyright:
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.
