Machine Learning Model for Computational Tracking and Forecasting the COVID-19 Dynamic Propagation

• Published in: IEEE journal of biomedical and health informatics Vol. 25; no. 3; pp. 615 - 622
• Main Authors: Gomes, Daiana Caroline dos Santos, Serra, Ginalber Luiz de Oliveira
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Adaptive filters; Algorithms; Clustering; Comparative analysis; Computational model; Computational modeling; Computer applications; Computer Simulation; Coronaviruses; COVID-19; COVID-19 - epidemiology; COVID-19 - transmission; COVID-19 - virology; Data models; epidemiological data; Epidemiology; Forecasting; Fuzzy Logic; Humans; interval type-2 fuzzy systems; kalman filtering; Kalman filters; Learning algorithms; Machine Learning; Mathematical models; Predictive models; Propagation; Real time; Real-time systems; SARS-CoV-2 - isolation & purification; Tracking; Viral diseases
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2021.3052134

A computational model with intelligent machine learning for analysis of epidemiological data, is proposed. The innovations of adopted methodology consist of an interval type-2 fuzzy clustering algorithm based on adaptive similarity distance mechanism for defining specific operation regions associated to the behavior and uncertainty inherited to epidemiological data, and an interval type-2 fuzzy version of Observer/Kalman Filter Identification (OKID) algorithm for adaptive tracking and real time forecasting according to unobservable components computed by recursive spectral decomposition of experimental epidemiological data. Experimental results and comparative analysis illustrate the efficiency and applicability of proposed methodology for adaptive tracking and real time forecasting the dynamic propagation behavior of novel coronavirus 2019 (COVID-19) outbreak in Brazil.