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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References
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). https://doi.org/10.3390/healthcare8010046 DOI: https://doi.org/10.3390/healthcare8010046
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. https://doi.org/10.1109/ABLAZE.2015.7154917 DOI: https://doi.org/10.1109/ABLAZE.2015.7154917
Ashrafian, H., & Darzi, A. (2018). Transforming health policy through machine learning. PLoS Medicine, 15(11), 10–13. https://doi.org/10.1371/journal.pmed.1002692 DOI: https://doi.org/10.1371/journal.pmed.1002692
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. https://doi.org/10.1002/rcs.1570 DOI: https://doi.org/10.1002/rcs.1570
Barnett-Howell, Z., & Mobarak, A. M. (2020). The Benefits and Costs of Social Distancing in Rich and Poor Countries. 1–3. http://arxiv.org/abs/2004.04867
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). https://doi.org/10.3390/jpm10020021 DOI: https://doi.org/10.3390/jpm10020021
BCG World Atlas. (2020). A database of global BCG vaccination policies and practices. http://bcgatlas.org/
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. https://doi.org/10.1093/ntr/ntaa059 DOI: https://doi.org/10.1093/ntr/ntaa059
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. https://doi.org/10.1101/2020.05.29.123612 DOI: https://doi.org/10.1101/2020.05.29.123612
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. https://doi.org/10.3390/PLANTS9060800 DOI: https://doi.org/10.3390/plants9060800
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. https://doi.org/10.3390/ijerph17155399 DOI: https://doi.org/10.3390/ijerph17155399
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. https://doi.org/10.3390/app10155135 DOI: https://doi.org/10.3390/app10155135
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. https://doi.org/10.1007/978-3-319-90503-7_10 DOI: https://doi.org/10.1007/978-3-319-90503-7_10
Cai, H. (2020). Sex difference and smoking predisposition in patients with COVID-19. The Lancet Respiratory Medicine, 8(4), e20. https://doi.org/10.1016/S2213-2600(20)30117-X DOI: https://doi.org/10.1016/S2213-2600(20)30117-X
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. https://doi.org/10.3390/ijerph17134677 DOI: https://doi.org/10.3390/ijerph17134677
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. https://doi.org/10.3390/su9122309 DOI: https://doi.org/10.3390/su9122309
Clifford, G. D. (2020). The Future AI in Healthcare: A Tsunami of False Alarms or a Product of Experts? 404, 1–49. http://arxiv.org/abs/2007.10502
Contini, D., & Costabile, F. (2020). Does air pollution influence COVID-19 outbreaks? Atmosphere, 11(4), 377. https://doi.org/10.3390/ATMOS11040377 DOI: https://doi.org/10.3390/atmos11040377
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. https://doi.org/10.1096/fj.202001115R DOI: https://doi.org/10.1096/fj.202001115R
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. https://doi.org/10.1007/978-3-319-69775-8_9 DOI: https://doi.org/10.1007/978-3-319-69775-8_9
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. https://doi.org/10.1200/cci.17.00130 DOI: https://doi.org/10.1200/CCI.17.00130
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. https://doi.org/10.1111/all.14345 DOI: https://doi.org/10.1111/all.14345
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. https://doi.org/10.20944/preprints202003.0364.v1 DOI: https://doi.org/10.20944/preprints202003.0364.v1
Hayden, J. C., & Parkin, R. (2020). The challenges of COVID-19 for community pharmacists and opportunities for the future. Irish Journal of Psychological Medicine. https://doi.org/10.1017/ipm.2020.52 DOI: https://doi.org/10.1017/ipm.2020.52
Helgi Library. (2020). Spice Consumption Per Capita. https://www.helgilibrary.com/indicators/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. https://doi.org/10.1038/s42256-020-0185-2 DOI: https://doi.org/10.1038/s42256-020-0185-2
Huber-Carol, C., Balakrishnan, N., M.S. Nikulin, & M. Mesbah. (2008). Statistics for Industry and Technology. In Statistics for Industry and Technology. https://doi.org/10.1007/978-0-8176-4619-6_22 DOI: https://doi.org/10.1007/978-0-8176-4619-6_22
Iwasaki, A., & Grubaugh, N. D. (2020). Why does Japan have so few cases of COVID‐19? EMBO Molecular Medicine, 12(5), 10–13. https://doi.org/10.15252/emmm.202012481 DOI: https://doi.org/10.15252/emmm.202012481
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. https://doi.org/10.1080/14787210.2020.1771181 DOI: https://doi.org/10.1080/14787210.2020.1771181
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. https://doi.org/10.1017/ice.2020.222 DOI: https://doi.org/10.1017/ice.2020.222
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). https://doi.org/10.1002/jmv.26035 DOI: https://doi.org/10.1002/jmv.26035
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. https://doi.org/10.3390/vaccines8030378 DOI: https://doi.org/10.3390/vaccines8030378
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. https://doi.org/10.1371/journal.pone.0233875 DOI: https://doi.org/10.1371/journal.pone.0233875
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. https://doi.org/10.1016/j.scitotenv.2020.138858 DOI: https://doi.org/10.1016/j.scitotenv.2020.138858
Mahase, E. (2020). Covid-19 : Russia approves vaccine without large scale testing or published results. August, 1–3. https://doi.org/10.1136/bmj.m3205 DOI: https://doi.org/10.1136/bmj.m3205
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. https://doi.org/10.1007/s41885-020-00070-3
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. https://doi.org/10.1007/s41885-020-00070-3 DOI: https://doi.org/10.1007/s41885-020-00070-3
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. https://doi.org/10.18488/journal.2.2020.102.96.104 DOI: https://doi.org/10.18488/journal.2.2020.102.96.104
Miyasaka, M. (2020). Is BCG vaccination causally related to reduced COVID‐19 mortality? EMBO Molecular Medicine, 12(6), 10–13. https://doi.org/10.15252/emmm.202012661 DOI: https://doi.org/10.15252/emmm.202012661
Molnar, C. (2019). Interpretable Machine Learning. Book, 247. https://christophm.github.io/interpretable-ml-book
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. https://doi.org/10.5958/0976-478x.2020.00010.5 DOI: https://doi.org/10.5958/0976-478X.2020.00010.5
Ooms, R., & Spruit, M. (2020). Self-service data science in healthcare with automated machine learning. Applied Sciences (Switzerland), 10(9). https://doi.org/10.3390/app10092992 DOI: https://doi.org/10.3390/app10092992
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. https://doi.org/10.3390/SU12103973 DOI: https://doi.org/10.3390/su12103973
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. https://doi.org/10.3390/ijerph17155354 DOI: https://doi.org/10.3390/ijerph17155354
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. https://doi.org/10.1002/jmv.26389 DOI: https://doi.org/10.1002/jmv.26389
Rocklöv, J., & Sjödin, H. (2020). High population densities catalyse the spread of COVID-19. Journal of Travel Medicine, 27(3), 1–2. https://doi.org/10.1093/jtm/taaa038 DOI: https://doi.org/10.1093/jtm/taaa038
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. https://doi.org/10.1002/rcs
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. https://doi.org/10.1186/s12939-020-01242-z DOI: https://doi.org/10.1186/s12939-020-01242-z
Saadat, S., Rawtani, D., & Hussain, C. M. (2020). Environmental perspective of COVID-19. Science of the Total Environment, 728, 138870. https://doi.org/10.1016/j.scitotenv.2020.138870 DOI: https://doi.org/10.1016/j.scitotenv.2020.138870
Saeb, S., Lonini, L., Jayaraman, A., Mohr, D., & Kording, K. (2016). Voodoo Machine Learning for Clinical Predictions. 059774. https://doi.org/10.1101/059774 DOI: https://doi.org/10.1101/059774
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. https://doi.org/10.1371/journal.pmed.1002721 DOI: https://doi.org/10.1371/journal.pmed.1002721
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. https://doi.org/10.1111/bph.15072 DOI: https://doi.org/10.1111/bph.15072
Sethi, A., & Bach, H. (2020). Evaluation of current therapies for COVID-19 treatment. Microorganisms, 8(8), 1–17. https://doi.org/10.3390/microorganisms8081097 DOI: https://doi.org/10.3390/microorganisms8081097
Sidor, A., & Rzymski, P. (2020). Dietary choices and habits during COVID-19 lockdown: Experience from Poland. Nutrients, 12(6), 1–14. https://doi.org/10.3390/nu12061657 DOI: https://doi.org/10.3390/nu12061657
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). https://doi.org/10.1136/bmjopen-2020-039426 DOI: https://doi.org/10.1136/bmjopen-2020-039426
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. https://doi.org/10.3390/ijerph17144924 DOI: https://doi.org/10.3390/ijerph17144924
Stiglic, G., Kocbek, P., Fijacko, N., Zitnik, M., Verbert, K., & Cilar, L. (2020). Interpretability of machine learning based prediction models in healthcare. In arXiv.org. Cornell University Library, arXiv.org. https://search.proquest.com/docview/2359831537?accountid=12669 DOI: https://doi.org/10.1002/widm.1379
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. https://doi.org/10.1002/psp4.12491 DOI: https://doi.org/10.1002/psp4.12491
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. https://doi.org/10.1111/ijcp.13649 DOI: https://doi.org/10.1111/ijcp.13649
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. https://doi.org/10.3390/ijerph17155589 DOI: https://doi.org/10.3390/ijerph17155589
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. https://doi.org/10.1186/s13690-020-00433-y DOI: https://doi.org/10.1186/s13690-020-00433-y
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. https://doi.org/10.1002/ptr.6738 DOI: https://doi.org/10.1002/ptr.6738
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. https://doi.org/10.1093/pcmedi/pbaa006 DOI: https://doi.org/10.1093/pcmedi/pbaa006
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. https://doi.org/10.1101/2020.02.20.20025338 DOI: https://doi.org/10.1101/2020.02.20.20025338
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). https://doi.org/10.1371/journal.pmed.1001012 DOI: https://doi.org/10.1371/journal.pmed.1001012