Validation Method of the Mathematical Model for SARS-Cov-2 Pandemic from Data Mining and Statistical Analysis
DOI:
https://doi.org/10.55747/bjedis.v1i2.48225Keywords:
Statistical analysis, Data mining, Epidemiology, Mathematical models, SARS-Cov-2, New coronavirus, Pandemic, Differential equationsAbstract
Nowadays, in 2020, we live during the most dangerous global pandemic that has been reported since the Spanish Flu, which occurred between 1918 and 1920. According to World Health Organization (WHO) records, the pandemic caused by the Sars-Cov-2 virus began in December 2019 and is present in all continents and almost all countries, surpassing more than 79 million infected and 1.7 million deaths by December 2020. Several mathematical models applied to Epidemiology have been adopted over time. One of the most widely adopted is the Susceptible-Infected-Recovered (SIR), developed in India by Kermack and Mckendrick in 1927. In our research, a comprehensive collection of data on the SARS-Cov-2 pandemic was made from reports by WHO, Dadax Limited (Chinese data company), and Johns Hopkins University in the United States of America (USA). Facts were collected from many different countries, regarding the number of confirmed, recovered, and death cases. In this article, we constructed a mathematical model that describes the evolution of the pandemic from the similarity with models already adopted in the field of nuclear physics. For the validation of the mathematical model, we chose information from Germany due to the reliability of the available information. Thus, a statistical analysis was executed to qualify the performance of the method and the predictive character of the mathematical model. To date, 11,716 raw data have been collected, of which we performed data mining relevant to use in this research.
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We at this moment declare that the present paper is our original work and has not been previously considered, either in whole or in part, for publication elsewhere. Besides, we warrant the authors will not submit this paper for publication in any other journal. We also guarantee that this article is free of plagiarism and that any accusation of plagiarism will be the authors' sole responsibility. The undersigned transfer all copyrights to the present paper (including without limitation the right to publish the work in any and all forms) to BJEDIS, understanding that neglecting this agreement will submit the violator to undertake the legal actions provided in the Law on Copyright and Neighboring Rights (No. 9610 of February 19, 1998). Also, we, the authors, declare no conflict of interest. Finally, all funders were cited in the acknowledgments section.
