548
Views
4
CrossRef citations to date
0
Altmetric
Research Article

Nations performance evaluation during SARS-CoV-2 outbreak handling via data envelopment analysis and machine learning methods

&
Article: 2022243 | Received 03 Jun 2021, Accepted 18 Dec 2021, Published online: 07 Jan 2022

References

  • Alinezhad, A., & Khalili, J. (2019). MAUT method. In C. C. Price, J. Zhu, & F. S. Hillier (Eds.), New methods and applications in multiple attribute decision making (MADM) (pp. 127–131). Springer.
  • Alinezhad, A., Makui, A., & Mavi, R. K. (2007). An inverse DEA model for inputs/outputs estimation with respect to decision maker’s preferences: The case of Refah bank of IRAN. Mathematical Sciences, 1(1–2), 61–70.
  • Alinezhad, A., Sarrafha, K., & Amini, A. (2014). Sensitivity analysis of SAW technique: The impact of changing the decision making matrix elements on the final ranking of alternatives. Iranian Journal of Operations Research, 5(1), 82–94.
  • Ardabili, S. F., Mosavi, A., Ghamisi, P., Ferdinand, F., Varkonyi-Koczy, A. R., Reuter, U., Rabczuk, T., & Atkinson, P. M. (2020). Covid-19 outbreak prediction with machine learning. Algorithms, 13(10), 249. https://doi.org/10.3390/a13100249
  • Aslani, B., Rabiee, M., & Tavana, M. (2020). An integrated information fusion and grey multi-criteria decision-making framework for sustainable supplier selection. International Journal of Systems Science: Operations & Logistics, 8(4), 348–370. https://doi.org/10.1080/23302674.2020.1776414
  • Aslani Khiavi, S., Hashemzadeh, F., & Khaloozadeh, H. (2021). Sensitivity analysis of the bullwhip effect in supply chains with time delay. International Journal of Systems Science: Operations & Logistics, 1–14. https://doi.org/10.1080/23302674.2021.1968064
  • Aydin, N., & Yurdakul, G. (2020). Assessing countries’ performances against COVID-19 via WSIDEA and machine learning algorithms. Applied Soft Computing, 97, Article 106792. https://doi.org/10.1016/j.asoc.2020.106792
  • Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078
  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
  • Ciotti, M., Ciccozzi, M., Terrinoni, A., Jiang, W. C., Wang, C. B., & Bernardini, S. (2020). The COVID-19 pandemic. Critical Reviews in Clinical Laboratory Sciences, 57(6), 365–388. https://doi.org/10.1080/10408363.2020.1783198
  • Doğan, Mİ, Özsoy, V. S., & Örkcü, H. H. (2021). Performance management of OECD countries on Covid-19 pandemic: A criticism using data envelopment analysis models. Journal of Facilities Management, 19(4), 479–499. https://doi.org/10.1108/JFM-01-2021-0005
  • Emrouznejad, A., & Shale, E. (2009). A combined neural network and DEA for measuring efficiency of large scale datasets. Computers & Industrial Engineering, 56(1), 249–254. https://doi.org/10.1016/j.cie.2008.05.012
  • Färe, R., & Grosskopf, S. (2004). Modeling undesirable factors in efficiency evaluation: Comment. European Journal of Operational Research, 157(1), 242–245. https://doi.org/10.1016/S0377-2217(03)00191-7
  • Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3), 253–281. https://doi.org/10.2307/2343100
  • IHME COVID-19 forecasting team. (2020). Modeling COVID-19 scenarios for the United States. Nature Medicine, 26(12), 1950. https://doi.org/10.1038/s41591-020-01181-w
  • Institute for Health Metrics and Evaluation. (2020a). COVID-19 projections. https://covid19.healthdata.org/
  • Institute for Health Metrics and Evaluation. (2020b). COVID-19 estimate downloads. http://www.healthdata.org/covid/data-downloads/
  • Jomthanachai, S., Wong, W. P., & Lim, C. P. (2021). An application of data envelopment analysis and machine learning approach to risk management. IEEE Access, 9, 85978–85994. https://doi.org/10.1109/ACCESS.2021.3087623
  • Kamel, M. A., & Mousa, M. E. S. (2021). Measuring operational efficiency of isolation hospitals during COVID-19 pandemic using data envelopment analysis: A case of Egypt. Benchmarking: An International Journal, 28(7), 2178–2201. https://doi.org/10.1108/BIJ-09-2020-0481
  • Kao, C. (2009). Efficiency decomposition in network data envelopment analysis: A relational model. European Journal of Operational Research, 192(3), 949–962. https://doi.org/10.1016/j.ejor.2007.10.008
  • Khalili, J., & Alinezhad, A. (2018). Performance evaluation in Green supply chain using BSC, DEA and data mining. International Journal of Supply and Operations Management, 5(2), 182–191. https://doi.org/10.22034/2018.2.6
  • Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
  • Kongar, E., & Adebayo, O. (2021). Impact of social media marketing on business performance: A hybrid performance measurement approach using data analytics and machine learning. IEEE Engineering Management Review, 49(1), 133–147. https://doi.org/10.1109/EMR.2021.3055036
  • Kretchy, I. A., Asiedu-Danso, M., & Kretchy, J. P. (2021). Medication management and adherence during the COVID-19 pandemic: Perspectives and experiences from low-and middle-income countries. Research in Social and Administrative Pharmacy, 17(1), 2023–2026. https://doi.org/10.1016/j.sapharm.2020.04.007
  • Kumar, A., Shrivastav, S. K., & Mukherjee, K. (2021). Performance evaluation of Indian banks using feature selection data envelopment analysis: A machine learning perspective. Journal of Public Affairs, e2686. https://doi.org/10.1002/pa.2686
  • Liermann, V., & Li, S. (2021). Methods of machine learning. In V. Liermann & C. Stegmann (Eds.), The digital journey of banking and insurance, volume III (pp. 225–238). Palgrave Macmillan.
  • Liu, D. C., & Nocedal, J. (1989). On the limited memory BFGS method for large scale optimization. Mathematical Programming, 45(1), 503–528. https://doi.org/10.1007/BF01589116
  • Man, H., & Jie, L. (2021). Comprehensive efficiency analysis of logistics industry based on machine learning and self-service data envelopment analysis model. Journal of Intelligent & Fuzzy Systems, 40(4), 6913–6924. https://doi.org/10.3233/JIFS-189522
  • MATrix LABoratory (MATLAB) software, version R. (2019). https://de.mathworks.com/
  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133. https://doi.org/10.1007/BF02478259
  • Mohammadi, S., Darestani, S. A., Vahdani, B., & Alinezhad, A. (2020). A robust neutrosophic fuzzy-based approach to integrate reliable facility location and routing decisions for disaster relief under fairness and aftershocks concerns. Computers & Industrial Engineering, 148, Article 106734. https://doi.org/10.1016/j.cie.2020.106734
  • Molla-Alizadeh-Zavardehi, S., Mahmoodirad, A., Sanei, M., Niroomand, S., & Banihashemi, S. (2020). Metaheuristics for data envelopment analysis problems. International Journal of Systems Science: Operations & Logistics, 8(4), 371–382. https://doi.org/10.1080/23302674.2020.1779381
  • Mourad, N., Habib, A., & Tharwat, A. (2021). Appraising healthcare systems’ efficiency in facing COVID-19 through data envelopment analysis. Decision Science Letters, 10(3), 301–310. https://doi.org/10.5267/j.dsl.2021.2.007
  • Nandy, A., & Singh, P. K. (2020). Application of fuzzy DEA and machine learning algorithms in efficiency estimation of paddy producers of rural eastern India. Benchmarking: An International Journal, 28(1), 229–248. https://doi.org/10.1108/BIJ-01-2020-0012
  • Nguyen, H. K., & Vu, M. N. (2021). Assess the impact of the COVID-19 pandemic and propose solutions for sustainable development for textile enterprises: An integrated data envelopment analysis-binary logistic model approach. Journal of Risk and Financial Management, 14(10), 465. https://doi.org/10.3390/jrfm14100465
  • Our World in Data. (2020a). Statistics and research, coronavirus (COVID-19) cases. https://ourworldindata.org/covid-cases
  • Our World in Data. (2020b). Statistics and research, coronavirus (COVID-19) deaths. https://ourworldindata.org/covid-deaths
  • Pinter, G., Felde, I., Mosavi, A., Ghamisi, P., & Gloaguen, R. (2020). COVID-19 pandemic prediction for Hungary; a hybrid machine learning approach. Mathematics, 8(6), 890. https://doi.org/10.3390/math8060890
  • Punn, N. S., Sonbhadra, S. K., & Agarwal, S. (2020). COVID-19 epidemic analysis using machine learning and deep learning algorithms. MedRxiv.
  • Python 3 programming language in Anaconda Navigator, Jupyter notebook (version 6.0.3). (2020). https://www.anaconda.com/products/individual
  • Revuelta, I., Santos-Arteaga, F., Di Caprio, D., Montagud-Marrahi, E., Cofan, F., Torregrosa, J., Bodro, M., Moreno, A., Ventura-Aguiar, P., Cucchiari, D., & Diekmann, F. (2021). A machine learning-based predictive model for outcome of Covid-19 in kidney transplant recipients. American Journal of Transplantation, 21(4), 463.
  • Sarrafha, K., Kazemi, A., & Alinezhad, A. (2014). A multi-objective evolutionary approach for integrated production-distribution planning problem in a supply chain network. Journal of Optimization in Industrial Engineering, 7(14), 89–102. https://dorl.net/dor/20.1001.1.22519904.2014.7.14.8.6
  • Scikit Learn. (2020). Neural network models (supervised). https://scikit-learn.org/stable/modules/neural_networks_supervised.html#multi-layer-perceptron/
  • Seddighi, H., Baharmand, H., Sharifabadi, A. M., Salmani, I., & Seddighi, S. (2021). Efficiency of COVID-19 testing centers in Iran: A data envelopment analysis approach. Disaster Medicine and Public Health Preparedness, 1–4. https://doi.org/10.1017/dmp.2021.226
  • Tayal, A., Solanki, A., & Singh, S. P. (2020). Integrated frame work for identifying sustainable manufacturing layouts based on big data, machine learning, meta-heuristic and data envelopment analysis. Sustainable Cities and Society, 62, Article 102383. https://doi.org/10.1016/j.scs.2020.102383
  • Tuli, S., Tuli, S., Tuli, R., & Gill, S. S. (2020). Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet of Things, 11, Article 100222. https://doi.org/10.1016/j.iot.2020.100222
  • World Health Organization. (2020a). Coronavirus disease (COVID-19) symptoms. https://www.who.int/health-topics/coronavirus#tab=tab_3
  • World Health Organization. (2020b). WHO coronavirus (COVID-19) dashboard. https://covid19.who.int/
  • Worldometer. (2020). Reported cases and deaths by country or territory. https://www.worldometers.info/coronavirus/.
  • Xu, Y., Park, Y. S., & Park, J. D. (2021). Measuring the response performance of US States against COVID-19 using an integrated DEA, CART, and logistic regression approach. Healthcare, 9(3), 268. https://doi.org/10.3390/healthcare9030268
  • Zeroual, A., Harrou, F., Dairi, A., & Sun, Y. (2020). Deep learning methods for forecasting COVID-19 time-Series data: A comparative study. Chaos, Solitons & Fractals, 140, Article 110121. https://doi.org/10.1016/j.chaos.2020.110121
  • Zhu, N., Zhu, C., & Emrouznejad, A. (2020). A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companies. Journal of Management Science and Engineering, 1–14. https://doi.org/10.1016/j.jmse.2020.10.001
  • Zoabi, Y., Deri-Rozov, S., & Shomron, N. (2021). Machine learning-based prediction of COVID-19 diagnosis based on symptoms. NPJ Digital Medicine, 4(1), 1–5. https://doi.org/10.1038/s41746-020-00372-6

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.