Technology Management for Accelerated Recovery during COVID-19
A Data-Driven Machine Learning Approach
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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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Copyright (c) 2020 Swapnil Morande, Dr. Veena Tewari
This work is licensed under a Creative Commons Attribution 4.0 International License.
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