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

Main Article Content

Swapnil Morande
https://orcid.org/0000-0003-4720-2980
Dr. Veena Tewari
https://orcid.org/0000-0003-4025-9909

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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Article Details

Morande, S., & Tewari, V. (2020). Technology Management for Accelerated Recovery during COVID-19: A Data-Driven Machine Learning Approach. SEISENSE Journal of Management, 3(5), 33-53. https://doi.org/10.33215/sjom.v3i5.445
Research Articles

Copyright (c) 2020 Swapnil Morande, Dr. Veena Tewari

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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