The State of the Art of Data Mining Algorithms for Predicting the COVID-19 Pandemic

• Published in: Axioms Vol. 11; no. 5; p. 242
• Main Authors: Cortés-Martínez, Keila Vasthi, Estrada-Esquivel, Hugo, Martínez-Rebollar, Alicia, Hernández-Pérez, Yasmín, Ortiz-Hernández, Javier
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2022
• Subjects: Algorithms; Comparative studies; Coronaviruses; COVID-19; Data mining; Disease transmission; Epidemics; Epidemiology; Equilibrium; Evolution; Infectious diseases; Machine learning; Mathematical models; Medical research; Natural language processing; pandemic; Pandemics; Prediction models; predictive epidemiological models; Scientific papers; Viral diseases
• ISSN: 2075-1680; 2075-1680
• DOI: 10.3390/axioms11050242

Current computer systems are accumulating huge amounts of information in several application domains. The outbreak of COVID-19 has increased rekindled interest in the use of data mining techniques for the analysis of factors that are related to the emergence of an epidemic. Data mining techniques are being used in the analysis and interpretation of information, which helps in the discovery of patterns, planning of isolation policies, and even predicting the speed of proliferation of contagion in a viral disease such as COVID-19. This research provides a comprehensive study of various data mining algorithms that are used in conjunction with epidemiological prediction models. The document considers that there is an opportunity to improve or develop tools that offer an accurate prognosis in the management of viral diseases through the use of data mining tools, based on a comparative study of 35 research papers.