A Novel Machine Learning Model for Adaptive Tracking and Real-Time Forecasting COVID-19 Dynamic Propagation

dos Santos Gomes, D. C.; de Oliveira Serra, G. L.

This chapter presents a computational model with intelligent machine learning for the analysis of epidemiological data. 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 with 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 the proposed methodology for adaptive tracking and real-time forecasting the dynamic propagation behavior of novel coronavirus 2019 (COVID-19) outbreak in Brazil. © 2021, Springer Nature Switzerland AG.