SEISENSE Journal of Management
Vol. 3 No. 5 (2020): SEISENSE Journal of Management

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

Morande, Swapnil (Unknown)
Tewari, Veena (Unknown)



Article Info

Publish Date
06 Sep 2020

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.

Copyrights © 2020






Journal Info

Abbrev

jom

Publisher

Subject

Economics, Econometrics & Finance Social Sciences

Description

SEISENSE Journal of Management (SJOM) peer-reviewed and published as Bi-Monthly (six issues in a year), is committed to publishing scholarly empirical and theoretical research articles that have a high impact on the management field as a whole. SEISENSE JoM covers domains such as Business strategy & ...