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

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.

Automated summary

A computational model using intelligent machine learning for analyzing epidemiological data is proposed, which includes an interval type-2 fuzzy clustering algorithm with an adaptive similarity distance mechanism and an interval type-2 fuzzy version of Observer/Kalman Filter Identification (OKID) algorithm for adaptive tracking and real-time forecasting. Experimental results show the efficiency and applicability of this methodology for tracking and forecasting the dynamic propagation behavior of COVID-19 outbreak in Brazil.