Pharmacy Impact on Covid-19 Vaccination Progress Using Machine Learning Approach
Shawni Dutta, Upasana Mukherjee, Samir Kumar Bandyopadhyay
Journal of Pharmaceutical Research International · pp. 202–217 · Published 24 Jul 2021
10.9734/jpri/2021/v33i38A32076Abstract
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. This deadly disease has put social, economic condition of the entire world into an enormous challenge. 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. The worldwide vaccination progress should be tracked to identify how fast the entire economic as well as social life will be stabilized. The monitor of the vaccination progress, a machine learning based Regressor model is approached in this study. This vaccination 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 Extra trees Regressor outperforms with minimized mean absolute error (MAE) of 6.465 and root mean squared error (RMSE) of 8.127. The uniqueness of this study relies on assessing as well as predicting vaccination intake progress by utilizing automated process offered by machine learning techniques. The innovative idea of the method is that the vaccination process and their priority are considered in the paper. Among several existing machine learning approaches, the ensemble based learning paradigms are employed in this study so that improved prediction efficiency can be delivered.
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