Best Practice Performance of COVID-19 in America continent with Artificial Intelligence

Amir Karbassi Yazdi; Hossein Komasi

doi:10.31181/sor1120241

Authors

Amir Karbassi Yazdi School of Engineering, Catholic University of the North, Coquimbo 1780000, Chile Author
Hossein Komasi School of Engineering, Catholic University of the North, Coquimbo 1780000, Chile Author

DOI:

https://doi.org/10.31181/sor1120241

Keywords:

COVID-19, Performance Measurement, ANFIS Method, K-means Method, Meta-Heuristics Method

Abstract

Metaheuristics were employed with ANFIS and K-means to determine whether COVID-19 performed the best on the American continent. A few individuals lost their lives in COVID-19, and quite a few nations assisted with this matter. It is essential to know the nations that performed the best in COVID-19. Researchers can carry out evaluations of nations using metaheuristic approaches and ANFIS. Based on the performance of these nations, clusters will be determined and established. The research excluded only two of the thirteen criteria that were investigated. Seven distinct groups have been established for each of the thirty-five nations. In the United States, the performance of COVID-19 is the poorest, according to research. These three nations also had the most extraordinary response to the COVID-19 outbreak. Based on the methodology and the context of the literature evaluation, this work's contribution may be divided into two distinct areas. The existence of research gaps makes it clear that a regional emphasis is needed rather than a focus on a nation or a portion of a country, which illustrates the requirement for a focus on the whole continent.

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References

Ali, S. A., Baloch, M., Ahmed, N., Ali, A. A., & Iqbal, A. (2020). The outbreak of Coronavirus Disease 2019 (COVID-19)—An emerging global health threat. Journal of infection and public health, 13(4), 644-646. https://doi.org/10.1016/j.jiph.2020.02.033

Klompas, M., Milton, D. K., Rhee, C., Baker, M. A., & Leekha, S. (2021). Current insights into respiratory virus transmission and potential implications for infection control programs: a narrative review. Annals of internal medicine, 174(12), 1710-1718. https://doi.org/10.7326/M21-2780

Bera, R. K. (2021). COVID-19 catalyzed disruptions to life and livelihood. Available at SSRN 3935942. https://doi.org/10.2139/ssrn.3935942

Parrott, D. J., Halmos, M. B., Stappenbeck, C. A., & Moino, K. (2022). Intimate partner aggression during the COVID-19 pandemic: Associations with stress and heavy drinking. Psychology of violence, 12(2), 95. https://doi.org/10.1037/vio0000395

Gupta, N., Agrawal, S., Ish, P., Mishra, S., Gaind, R., Usha, G., Singh, B., Sen, M.K ., & Safdarjung Hospital COVID 2019 working group. (2020). Clinical and epidemiologic profile of the initial COVID-19 patients at a tertiary care centre in India. Monaldi archives for chest disease, 90(1). https://doi.org/10.4081/monaldi.2020.1294

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

Malki, Z., Atlam, E. S., Ewis, A., Dagnew, G., Ghoneim, O. A., Mohamed, A. A., Abdel Daim, M. M., & Gad, I. (2021). The COVID-19 pandemic: prediction study based on machine learning models. Environmental science and pollution research, 28, 40496-40506. https://doi.org/10.1007/s11356-021-13824-7

Goswami, K., Bharali, S., & Hazarika, J. (2020). Projections for COVID-19 pandemic in India and effect of temperature and humidity. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(5), 801-805. https://doi.org/10.1016/j.dsx.2020.05.045

Huang, J., Zhang, L., Liu, X., Wei, Y., Liu, C., Lian, X., Huang, Z., Chou, J.,Liu, X., Li, X., Yang, K., Wang, J., Liang, H., Gu, Q., Du, P., & Zhang, T. (2020). Global prediction system for COVID-19 pandemic. Science bulletin, 65(22), 1884. https://doi.org/10.1016/j.scib.2020.08.002

Fakhruddin, B. S., Blanchard, K., & Ragupathy, D. (2020). Are we there yet? The transition from response to recovery for the COVID-19 pandemic. Progress in Disaster Science, 7, 100102. https://doi.org/10.1016/j.pdisas.2020.100102

Milojević, I. (2021). COVID-19 and Pandemic Preparedness: Foresight Narratives and Public Sector Responses. Journal of Futures Studies, 26(1), 1-18. https://doi.org/10.6531/JFS.202109_26(1).0001

Al-Ma'seb, H., Al-Sejari, M., & Kondrat, D. (2023). The effects of the COVID-19 pandemic on the lives and social daily life of married individuals during the total curfew in Kuwait. Journal of Affective Disorders Reports, 14, 100676. https://doi.org/10.1016/j.jadr.2023.100676

Alizadeh, H., Sharifi, A., Damanbagh, S., Nazarnia, H., & Nazarnia, M. (2023). Impacts of the COVID-19 pandemic on the social sphere and lessons for crisis management: a literature review. Natural Hazards, 117(3), 2139-2164. https://doi.org/10.1007/s11069-023-05959-2

Hosseinzadeh, P., Zareipour, M., Baljani, E., & Moradali, M. R. (2022). Social consequences of the COVID-19 pandemic. A systematic review. Investigacion y educacion en enfermeria, 40(1), e10. https://doi.org/10.17533/udea.iee.v40n1e10

McKibbin, W., & Fernando, R. (2023). The global economic impacts of the COVID-19 pandemic. Economic Modelling, 129, 106551. https://doi.org/10.1016/j.econmod.2023.106551

Sharma, S., Gupta, Y. K., & Mishra, A. K. (2023). Analysis and prediction of COVID-19 multivariate data using deep ensemble learning methods. International Journal of Environmental Research and Public Health, 20(11), 5943. https://doi.org/10.3390/ijerph20115943

Sankalpa, D., Dhou, S., Pasquier, M., & Sagahyroon, A. (2024). Predicting the Spread of a Pandemic Using Machine Learning: A Case Study of COVID-19 in the UAE. Applied Sciences, 14(10), 4022. https://doi.org/10.3390/app14104022

Matta, D. M., & Saraf, M. K. (2020). Prediction of COVID-19 using machine learning techniques.

De Souza, F. S. H., Hojo-Souza, N. S., Dos Santos, E. B., Da Silva, C. M., & Guidoni, D. L. (2021). Predicting the disease outcome in COVID-19 positive patients through Machine Learning: a retrospective cohort study with Brazilian data. Frontiers in Artificial Intelligence, 4, 579931. https://doi.org/10.3389/frai.2021.579931

De Souza Andrade, H., Silva, M. B., de Freitas Chagas Jr, M., Rosa, A. C. M., & Chimendes, V. C. G. Proposal for a technology vigilance system for a Technology License Office. International Journal of Advanced Engineering Research and Science, 4(10), 237285. https://doi.org/10.22161/ijaers.4.10.23

Pal, N. R., Pal, K., & Bezdek, J. C. (1997, July). A mixed c-means clustering model. In Proceedings of 6th international fuzzy systems conference (Vol. 1, pp. 11-21). IEEE. https://doi.org/10.1109/FUZZY.1997.616338

ShahrAeini, S. A., Tabrizi, S., Birau, R., Spulbar, C., & Yazdi, A. K. (2021). New Data Mining approach for clustering Export Credit Agencies (ECAs) based on performance criteria: a bibliometric citation analysis for the period 2005 to 2020. Annals of the University of Craiova-Mathematics and Computer Science Series, 48(2), 374-383. https://doi.org/10.52846/ami.v48i1.1579

Jiao, L., Yang, H., Liu, Z. G., & Pan, Q. (2022). Interpretable fuzzy clustering using unsupervised fuzzy decision trees. Information Sciences, 611, 540-563. https://doi.org/10.1016/j.ins.2022.08.077

Sadeghi, B., Cheung, R. C., & Hanbury, M. (2021). Using hierarchical clustering analysis to evaluate COVID-19 pandemic preparedness and performance in 180 countries in 2020. BMJ open, 11(11), e049844. https://doi.org/10.1136/bmjopen-2021-049844

San-Cristobal, R., Martín-Hernández, R., Ramos-Lopez, O., Martinez-Urbistondo, D., Micó, V., Colmenarejo, G., Fernandez, P. V., Daimiel, L., & Martínez, J. A. (2022). Longwise cluster analysis for the prediction of COVID-19 severity within 72 h of admission: COVID-DATA-SAVE-LIFES cohort. Journal of Clinical Medicine, 11(12), 3327. https://doi.org/10.3390/jcm11123327

Tan, Y., Shuai, C., Jiao, L., & Shen, L. (2017). An adaptive neuro-fuzzy inference system (ANFIS) approach for measuring country sustainability performance. Environmental Impact Assessment Review, 65, 29-40. https://doi.org/10.1016/j.eiar.2017.04.004

Buragohain, M., & Mahanta, C. (2008). A novel approach for ANFIS modelling based on full factorial design. Applied soft computing, 8(1), 609-625. https://doi.org/10.1016/j.asoc.2007.03.010

Karbassi Yazdi, A., Kaviani, M. A., Emrouznejad, A., & Sahebi, H. (2020). A binary particle swarm optimization algorithm for ship routing and scheduling of liquefied natural gas transportation. Transportation Letters, 12(4), 223-232. https://doi.org/10.1080/19427867.2019.1581485

Fernández-Brillet, L., Álvarez, O., & Fernández-Martínez, J. L. (2021). The PSO Family: Application to the Portfolio Optimization Problem. In Applying Particle Swarm Optimization: New Solutions and Cases for Optimized Portfolios (pp. 111-132). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-70281-6_7