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

Main Article Content

Swapnil Morande
Dr. Veena Tewari


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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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.
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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.

Ahmed, J., Jaman, H., Saha, G., Ghosh, P., Hasnat, J., Saha, G., & Ghosh, P. (2020). Effect of Temperatures, Humidity and Population Density on the Spreading of Covid-19 at 70 Cities/Provinces. Preprints, June, 1–11.

Ahsen, M. E., Vogel, R. M., & Stolovitzky, G. A. (2019). Unsupervised evaluation and weighted aggregation of ranked classification predictions. Journal of Machine Learning Research, 20, 2018–2020.

Allam, Z., & Jones, D. S. (2020). On the Coronavirus (COVID-19) Outbreak and the Smart City Network: Universal Data Sharing Standards Coupled with Artificial Intelligence (AI) to Benefit Urban Health Monitoring and Management. In Healthcare (Vol. 8, Issue 1). DOI:

Arpitha, M. S., Mithun, K. A., Rakesh, S., Singh, A., & Yadav, A. (2018). Better Healthcare using Machine Learning. International Journal of Advanced Research in Computer Science, 9(3), 10–14. DOI:

Ashrafian, H., & Darzi, A. (2018). Transforming health policy through machine learning. PLoS Medicine, 15(11), 10–13. DOI:

Ashrafian, H., Darzi, A., & Athanasiou, T. (2015). A novel modification of the Turing test for artificial intelligence and robotics in healthcare. The International Journal of Medical Robotics and Computer Assisted Surgery, 11(1), 38–43. DOI:

Barnett-Howell, Z., & Mobarak, A. M. (2020). The Benefits and Costs of Social Distancing in Rich and Poor Countries. 1–3.

Battineni, G., Sagaro, G. G., Chinatalapudi, N., & Amenta, F. (2020). Applications of machine learning predictive models in the chronic disease diagnosis. Journal of Personalized Medicine, 10(2). DOI:

BCG World Atlas. (2020). A database of global BCG vaccination policies and practices.

Berlin, I., Thomas, D., Le Faou, A. L., & Cornuz, J. (2020). COVID-19 and smoking. Nicotine & Tobacco Research : Official Journal of the Society for Research on Nicotine and Tobacco, 1–3. DOI:

Bluhm, A., Christandl, M., Gesmundo, F., Ravn Klausen, F., Mancinska, L., Steffan, V., Stilck Franca, D., & Werner, A. (2020). SARS-CoV-2 transmission chains from genetic data: a Danish case study. BioRxiv, December 2019, 2020.05.29.123612. DOI:

Boukhatem, M. N., & Setzer, W. N. (2020). Aromatic herbs, medicinal plant-derived essential oils, and phytochemical extracts as potential therapies for coronaviruses: Future perspectives. Plants, 9(6), 1–23. DOI:

Bukhari, Q., Massaro, J. M., D’Agostino, R. B., & Khan, S. (2020). Effects of Weather on Coronavirus Pandemic. International Journal of Environmental Research and Public Health, 17(15), 5399. DOI:

Caballé, N. C., Castillo-Sequera, J. L., Gómez-Pulido, J. A., Gómez-Pulido, J. M., & Polo-Luque, M. L. (2020). Machine learning applied to diagnosis of human diseases: A systematic review. Applied Sciences (Switzerland), 10(15), 1–28. DOI:

Cabitza, F., Ciucci, D., & Rasoini, R. (2019). A giant with feet of clay: On the validity of the data that feed machine learning in medicine. Lecture Notes in Information Systems and Organisation, 28, 121–136. DOI:

Cai, H. (2020). Sex difference and smoking predisposition in patients with COVID-19. The Lancet Respiratory Medicine, 8(4), e20. DOI:

Chodkiewicz, J., Talarowska, M., Miniszewska, J., Nawrocka, N., & Bilinski, P. (2020). Alcohol consumption reported during the COVID-19 pandemic: The initial stage. International Journal of Environmental Research and Public Health, 17(13), 1–11. DOI:

Chui, K. T., Alhalabi, W., Pang, S. S. H., de Pablos, P. O., Liu, R. W., & Zhao, M. (2017). Disease diagnosis in smart healthcare: Innovation, technologies and applications. Sustainability (Switzerland), 9(12), 1–24. DOI:

Clifford, G. D. (2020). The Future AI in Healthcare: A Tsunami of False Alarms or a Product of Experts? 404, 1–49.

Contini, D., & Costabile, F. (2020). Does air pollution influence COVID-19 outbreaks? Atmosphere, 11(4), 377. DOI:

Debnath, M., Banerjee, M., & Berk, M. (2020). Genetic gateways to COVID-19 infection: Implications for risk, severity, and outcomes. FASEB Journal, 34(7), 8787–8795. DOI:

Feldman, K., Faust, L., Wu, X., Huang, C., & Chawla, N. V. (2017). Beyond volume: The impact of complex healthcare data on the machine learning pipeline. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10344 LNAI, 150–169. DOI:

Goshen, R., Choman, E., Ran, A., Muller, E., Kariv, R., Chodick, G., Ash, N., Narod, S., & Shalev, V. (2018). Computer-Assisted Flagging of Individuals at High Risk of Colorectal Cancer in a Large Health Maintenance Organization Using the ColonFlag Test. JCO Clinical Cancer Informatics, 2, 1–8. DOI:

Gursel, M., & Gursel, I. (2020). Is global BCG vaccination-induced trained immunity relevant to the progression of SARS-CoV-2 pandemic? Allergy: European Journal of Allergy and Clinical Immunology, 75(7), 1815–1819. DOI:

Han, Y., Lam, J. C. K., Li, V. O. K., Guo, P., & Zhang, Q. (2020). The effects of outdoor air pollution concentrations and lockdowns on Covid-19 infections in Wuhan and other provincial capitals in China. Preprints.Org, March, 1–7. DOI:

Hayden, J. C., & Parkin, R. (2020). The challenges of COVID-19 for community pharmacists and opportunities for the future. Irish Journal of Psychological Medicine. DOI:

Helgi Library. (2020). Spice Consumption Per Capita.

Hu, Y., Jacob, J., Parker, G. J. M., Hawkes, D. J., Hurst, J. R., & Stoyanov, D. (2020). The challenges of deploying artificial intelligence models in a rapidly evolving pandemic. Nature Machine Intelligence, 2(6), 298–300. DOI:

Huber-Carol, C., Balakrishnan, N., M.S. Nikulin, & M. Mesbah. (2008). Statistics for Industry and Technology. In Statistics for Industry and Technology. DOI:

Iwasaki, A., & Grubaugh, N. D. (2020). Why does Japan have so few cases of COVID‐19? EMBO Molecular Medicine, 12(5), 10–13. DOI:

Jean, S. S., & Hsueh, P. R. (2020). Old and re-purposed drugs for the treatment of COVID-19. Expert Review of Anti-Infective Therapy, 1–3. DOI:

Jiang, Y., Wu, X. J., & Guan, Y. J. (2020). Effect of ambient air pollutants and meteorological variables on COVID-19 incidence. Infection Control and Hospital Epidemiology, 1–5. DOI:

Karadag, E. (2020). Increase in COVID-19 cases and case-fatality and case-recovery rates in Europe: A cross-temporal meta-analysis. In Journal of Medical Virology (Issue December 2019). DOI:

Klinger, D., Blass, I., Rappoport, N., & Linial, M. (2020). Significantly improved COVID-19 outcomes in countries with higher bcg vaccination coverage: A multivariable analysis. Vaccines, 8(3), 1–14. DOI:

Livadiotis, G. (2020). Statistical analysis of the impact of environmental temperature on the exponential growth rate of cases infected by COVID-19. PLoS ONE, 15(5), 1–22. DOI:

Madurai Elavarasan, R., & Pugazhendhi, R. (2020). Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic. The Science of the Total Environment, 725, 138858. DOI:

Mahase, E. (2020). Covid-19 : Russia approves vaccine without large scale testing or published results. August, 1–3. DOI:

Martin, A., Markhvida, M., Hallegatte, S., & Walsh, B. (2020a). Socio-Economic Impacts of COVID-19 on Household Consumption and Poverty. Economics of Disasters and Climate Change.

Martin, A., Markhvida, M., Hallegatte, S., & Walsh, B. (2020b). Socio-Economic Impacts of COVID-19 on Household Consumption and Poverty. Economics of Disasters and Climate Change, 1–3. DOI:

Mhalla, M. (2020). The Impact of Novel Coronavirus (COVID-19) on the Global Oil and Aviation Markets. Journal of Asian Scientific Research, 10(2), 96–104. DOI:

Miyasaka, M. (2020). Is BCG vaccination causally related to reduced COVID‐19 mortality? EMBO Molecular Medicine, 12(6), 10–13. DOI:

Molnar, C. (2019). Interpretable Machine Learning. Book, 247.

Morande, S., & Pietronudo, M. C. (2020). Pervasive Health Systems: Convergence through Artificial Intelligence and Blockchain Technologies. Journal of Commerce and Management Thought, 11(2), 155. DOI:

Ooms, R., & Spruit, M. (2020). Self-service data science in healthcare with automated machine learning. Applied Sciences (Switzerland), 10(9). DOI:

Phillipson, J., Gorton, M., Turner, R., Shucksmith, M., Aitken-McDermott, K., Areal, F., Cowie, P., Hubbard, C., Maioli, S., McAreavey, R., Souza-Monteiro, D., Newbery, R., Panzone, L., Rowe, F., & Shortall, S. (2020). The COVID-19 pandemic and its implications for rural economies. Sustainability (Switzerland), 12(10), 1–10. DOI:

Rashed, E. A., Kodera, S., Gomez-Tames, J., & Hirata, A. (2020). Influence of absolute humidity, temperature and population density on COVID-19 spread and decay durations: Multi-prefecture study in Japan. International Journal of Environmental Research and Public Health, 17(15), 1–14. DOI:

Reddy, R. K., Charles, W. N., Sklavounos, A., Dutt, A., Seed, P. T., & Khajuria, A. (2020). The effect of smoking on COVID-19 severity: a systematic review and meta-analysis. Journal of Medical Virology, 0–2. DOI:

Rocklöv, J., & Sjödin, H. (2020). High population densities catalyse the spread of COVID-19. Journal of Travel Medicine, 27(3), 1–2. DOI:

Rodr, E., Kypson, A. P., Moten, S. C., Nifong, L. W., & Jr, W. R. C. (2006). Robotic mitral surgery at East Carolina University : International Journal, April, 211–215.

Rozenfeld, Y., Beam, J., Maier, H., Haggerson, W., Boudreau, K., Carlson, J., & Medows, R. (2020). A model of disparities: risk factors associated with COVID-19 infection. International Journal for Equity in Health, 19(1), 126. DOI:

Saadat, S., Rawtani, D., & Hussain, C. M. (2020). Environmental perspective of COVID-19. Science of the Total Environment, 728, 138870. DOI:

Saeb, S., Lonini, L., Jayaraman, A., Mohr, D., & Kording, K. (2016). Voodoo Machine Learning for Clinical Predictions. 059774. DOI:

Saria, S., Butte, A., & Sheikh, A. (2018). Better medicine through machine learning: What’s real, and what’s artificial? PLoS Medicine, 15(12), 1–6. DOI:

Scavone, C., Brusco, S., Bertini, M., Sportiello, L., Rafaniello, C., Zoccoli, A., Berrino, L., Racagni, G., Rossi, F., & Capuano, A. (2020). Current pharmacological treatments for COVID-19: what’s next? British Journal of Pharmacology. DOI:

Sethi, A., & Bach, H. (2020). Evaluation of current therapies for COVID-19 treatment. Microorganisms, 8(8), 1–17. DOI:

Sidor, A., & Rzymski, P. (2020). Dietary choices and habits during COVID-19 lockdown: Experience from Poland. Nutrients, 12(6), 1–14. DOI:

Silva Junior, F. J. G. Da, Sales, J. C. E. S., Monteiro, C. F. D. S., Costa, A. P. C., Campos, L. R. B., Miranda, P. I. G., Monteiro, T. A. D. S., Lima, R. A. G., & Lopes-Junior, L. C. (2020). Impact of COVID-19 pandemic on mental health of young people and adults: A systematic review protocol of observational studies. BMJ Open, 10(7). DOI:

Solomou, I., & Constantinidou, F. (2020). Prevalence and predictors of anxiety and depression symptoms during the COVID-19 pandemic and compliance with precautionary measures: Age and sex matter. International Journal of Environmental Research and Public Health, 17(14), 1–19. DOI:

Stiglic, G., Kocbek, P., Fijacko, N., Zitnik, M., Verbert, K., & Cilar, L. (2020). Interpretability of machine learning based prediction models in healthcare. In Cornell University Library, DOI:

Szabo, G., & Saha, B. (2015). Alcohol’s effect on host defense. Alcohol Research: Current Reviews, 37(2), 159–170.

Talevi, A., Morales, J. F., Hather, G., Podichetty, J. T., Kim, S., Bloomingdale, P. C., Kim, S., Burton, J., Brown, J. D., Winterstein, A. G., Schmidt, S., White, J. K., & Conrado, D. J. (2020). Machine Learning in Drug Discovery and Development Part 1: A Primer. CPT: Pharmacometrics and Systems Pharmacology, 9(3), 129–142. DOI:

Talukder, A., Author, C., Address, D., & Author, C. (2019). Title Page Effect of Age on Death Due to Coronavirus Disease 2019 (COVID-19): Application of Poisson Regression Model Running Head: Effect of Age on Death due to Coronavirus Disease 2019 (COVID-. 0–2. DOI:

Tan, Y., Jin, B., Yue, X., Chen, Y., & Sangiovanni-Vincentelli, A. (2020). Exploiting Uncertainties from Ensemble Learners to Improve Decision-Making in Healthcare AI. 1–3.

Urashima, M., Otani, K., Hasegawa, Y., & Akutsu, T. (2020). BCG Vaccination and Mortality of COVID-19 across 173 Countries: An Ecological Study. International Journal of Environmental Research and Public Health, 17(15), 1–21. DOI:

Wyper, G. M. A., Assunção, R., Cuschieri, S., Devleeschauwer, B., Fletcher, E., Haagsma, J. A., Hilderink, H. B. M., Idavain, J., Lesnik, T., Von Der Lippe, E., Majdan, M., Milicevic, M. S., Pallari, E., Peñalvo, J. L., Pires, S. M., Plaß, D., Santos, J. V., Stockton, D. L., Thomsen, S. T., & Grant, I. (2020). Population vulnerability to COVID-19 in Europe: A burden of disease analysis. Archives of Public Health, 78(1), 1–9. DOI:

Zahedipour, F., Hosseini, S. A., Sathyapalan, T., Majeed, M., Jamialahmadi, T., Al-Rasadi, K., Banach, M., & Sahebkar, A. (2020). Potential effects of curcumin in the treatment of COVID-19 infection. Phytotherapy Research. DOI:

Zhang, J., Wu, W., Zhao, X., & Zhang, W. (2020). Recommended psychological crisis intervention response to the 2019 novel coronavirus pneumonia outbreak in China: a model of West China Hospital. Precision Clinical Medicine, 3(1), 3–8. DOI:

Zhu, Z., Xu, S., Wang, H., Liu, Z., Wu, J., Li, G., Miao, J., Zhang, C., Yang, Y., Sun, W., Zhu, S., Fan, Y., Hu, J., Liu, J., & Wang, W. (2020). COVID-19 in Wuhan: Immediate Psychological Impact on 5062 Health Workers. 1095. DOI:

Zwerling, A., Behr, M. A., Verma, A., Brewer, T. F., Menzies, D., & Pai, M. (2011). The BCG world atlas: A database of global BCG vaccination policies and practices. PLoS Medicine, 8(3). DOI: