| Autor: |
Swapnil Morande, Veena Tewari |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
SEISENSE Journal of Management, Vol 3, Iss 5 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2617-5770 |
| DOI: |
10.33215/sjom.v3i5.445 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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