Analyze the Spread of Coronavirus in the World to Predict New Cases Under Machine Learning Techniques

Analyze the Spread of Coronavirus in the World to Predict New Cases Under Machine Learning Techniques

  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : Margarita Giraldo-Retuerto, Lilian Ocares-Cunyarachi, Alexandra Santisteban-Santisteban, Brian Malaver-Tuero, Erick Canova-Rosales, Alexis Delgado, Enrique Lee Huamaní
DOI : 10.14445/22315381/IJETT-V70I7P245

How to Cite?

Margarita Giraldo-Retuerto, Lilian Ocares-Cunyarachi, Alexandra Santisteban-Santisteban, Brian Malaver-Tuero, Erick Canova-Rosales, Alexis Delgado, Enrique Lee Huamaní, "Analyze the Spread of Coronavirus in the World to Predict New Cases Under Machine Learning Techniques" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 438-448, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P245

Abstract
Coronavirus is a worldwide pandemic disease. At the same time, it is making unexpected changes in different countries of the World, with new variants in each region due to their autonomous climates. That is why the coronavirus is mutating, and there is a massive contagion that causes death. Consequently, it is necessary to analyze and identify where the new cases occurred and where is the possible area of attack of the new variant of covid 19. It is also necessary to know the characteristics and the stage of infection of patients with covid 19. This research method is based on a branch of artificial intelligence, machine learning; the idea is to use artificial intelligence techniques to analyze and predict new coronavirus cases, using classification models, decision trees, and the Bernoulli model. The case study was used to input a real-time database with a systematic record of covid-19 from 2020 to the present. Accordingly, the data and properties for implementing the model and training were defined to make the corresponding predictions of new cases of covid 19. Finally, as a final result, predictions of the number of new cases and total deaths of covid 19 in the World were made. Finally, this research aims to analyze the data on the spread of Covid-19 in the World to predict new cases and help society prevent new variants of Covid 19 by using artificial intelligence to provide various related solutions.

Keywords
Covid-19, Coronavirus, Contagion, Data, Machine learning.

Reference
[1] N. Zhu, D. Zhang, W. Wang, X. Li, B. Yang, J. Song, X. Zhao, B. Huang, W. Shi, R. Lu, “A Novel Coronavirus From Patients with Pneumonia In China,” New England Journal of Medicine, 2020.
[2] M. Yadav, M. Perumal, and M. Srinivas, “Analysis on Novel Coronavirus (Covid-19) Using Machine Learning Methods,” Chaos, Solitons Fractals, vol. 139, pp.110050, 2020.
[3] A. E. Gorbalenya, S. C. Baker, R. Baric, R. J. D. Groot, C. Drosten, A. A. Gulyaeva, B. L. Haagmans, C. Lauber, A. M. Leontovich, B. W. Neuman, “Severe Acute Respiratory Syndrome-Related Coronavirus: the Species and Its Viruses–A Statement of the Coronavirus Study Group,” 2020.
[4] T. P. Velavan and C. G. Meyer, “the Covid-19 Epidemic,” Tropical Medicine & International Health, vol. 25, no. 3, pp. 278, 2020.
[5] Q. Li, X. Guan, P. Wu, X. Wang, L. Zhou, Y. Tong, R. Ren, K. S. Leung, E. H. Lau, J. Y. Wonget Al, “Early Transmission Dynamics In Wuhan, China, of Novel Coronavirus–Infected Pneumonia,” New England Journalof Medicine, 2020.
[6] A. E. Gorbalenya, S. C. Baker, R. Baric, R. J. D. Groot C. Drosten, A. A. Gulyaeva, B. L. Haagmans, C. Lauber A. M. Leontovich, B. W. Neuman, “Severe Acute Respiratory Syndrome-Related Coronavirus: the Species and Its Viruses–A Statement of the Coronavirus Study Group,” 2020.
[7] L. M. P. Da Silva Francisco, V. D. A. Borges, and D. D.S. V. Neto, “Aplicación De Técnica De Machine Learning Para Previsado Da Curva De Dados Da Covid-19,” 2020.
[8] G. Bonaccorso, “Machine Learning Algorithms,” 2017.
[9] A. Canabarro, F. F. Fanchini, A. L. Malvezzi, R. Pereira, and R. Chaves, “Unveiling Phase Transitions with Machine Learning,” Physical Review B, vol. 100, no. 4, P. 045129, 2019.
[10] R. E. Andrade Ramos and J. C. Cañar Zumba, “Procesamiento De Datos Mediante Machine Learning De Matlab,” B.S. Thesis, 2019.
[11] C. Russo, H. D. Ramon, N. Alonso, L. B. Cicerchia, L. Española, and J. P. Tessore, “Mass Data Processing Using Machine Learning Techniques,” In Xviii Workshop of Researchers In Computer Science (Entre Ríos , Argentina),2016.
[12] M.I. A. Oyarzabal Alcain Et Al, “Evaluation of the Impact of Containment Measures on the Spread of the Covid-19 Disease Through ’E Machine Learning Techniques A´ Ethical,” 2020.
[13] J. Hopkins, “Dashboard By the Center For Systems Science and Engineering University (Jhu),” 2021.
[14] M.I. A. Oyarzabal Alcain Et Al., “Evaluation of the Impact of Containment Measures on the Spread of the Covid-19 Disease Through ’E Machine Learning Techniques A´ Ethical,” 2020.
[15] J. Rabbah, M. Ridouani, and L. Hassouni, “A New Classification Model Based on Stacknet and Deep Learning For the Rapid Detection of Covid 19 Through X- Ray Images,” In 2020 Fourth International Conference on Intelligent Computing In Data Sciences (Icds), 2020, pp. 1–8.
[16] T. M. Mitchell Et Al., “Machine Learning,” 1997.
[17] E. Nazarenko, V. Varkentin, and T. Polyakova, “Features of Application of Machine Learning Methods For Classification of Network Traffic (Features, Advantages, Disadvantages),” In 2019 International Multi-Conference on Industrial Engineering and Modern Technologies (Fareastcon), 2019, pp. 1–5.
[18] A. Valdivieso, C. Díaz, and J. Sarmiento, “Mhealth with Bigdata and Machine Learning As Technological Support For Health In Colombia,” Loginn Magazine: Research.
[19] D. Liu, L. Clemente, C. Poirier, X. Ding, M. Chinazzi, J. T. Davis, A. Vespignani, and M. Santillana, “A Machine Learning Methodology For Real-Time Forecasting of the 2019-2020 Covid-19 Outbreak Using Internet Searches, News Alerts, and Estimates From Mechanistic Models,” Arxiv Preprint Arxiv:2004.04019, 2020.
[20] E. Parra, C. Dimou, J. Llorens, V. Moreno, and A. Fraga, “A Methodology For the Classification of Quality of Requirements Using Machine Learning Techniques,” Information and Software Technology, vol. 67, pp. 180–195, 2015.
[21] X.-D. Zhang, “Machine Learning,” In A Matrix Algebra Approach To Artificial Intelligence, Springer, 2020, pp. 223–440.