Technology Management for Accelerated Recovery during COVID-19: A Data-Driven Machine Learning Approach

Swapnil Morande; Veena Tewari

doi:10.33215/sjom.v3i5.445

Authors

Swapnil Morande
swapnil.morande@unina.it
University of Naples, Italy https://orcid.org/0000-0003-4720-2980
Dr. Veena Tewari Majan University College, Oman https://orcid.org/0000-0003-4025-9909

Keywords:

Machine Learning, Artificial Intelligence, Healthcare, Predictive Analytics, COVID-19

Abstract

Objective- The research looks forward to extracting strategies for accelerated recovery during the ongoing Covid-19 pandemic.

Design - Research design considers quantitative methodology and evaluates significant factors from 170 countries to deploy supervised and unsupervised Machine Learning techniques to generate non-trivial predictions.

Findings - Findings presented by the research reflect on data-driven observation applicable at the macro level and provide healthcare-oriented insights for governing authorities.

Policy Implications - Research provides interpretability of Machine Learning models regarding several aspects of the pandemic that can be leveraged for optimizing treatment protocols.

Originality - Research makes use of curated near-time data to identify significant correlations keeping emerging economies at the center stage. Considering the current state of clinical trial research reflects on parallel non-clinical strategies to co-exist with the Coronavirus.

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