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The Nonlinear Grey Bernoulli Model NGBM(1, 1) is a recently developed greymodel which has various applications in different fields, mainly due to itsaccuracy in handling small time-series datasets with nonlinear variations. Inthis paper, to fully improve the accuracy of this model, a novel model isproposed, namely Rolling Optimized Nonlinear Grey Bernoulli Model RONGBM(1, 1).This model combines the rolling mechanism with the simultaneous optimization ofall model parameters (exponential, background value and initial condition). Theaccuracy of this new model has significantly been proven through forecastingVietnam's GDP from 2013 to 2018, before it is applied to predict the totalCOVID-19 infected cases globally by day.

A Rolling Optimized Nonlinear Grey Bernoulli Model RONGBM(1,1) and application in predicting total COVID-19 infected cases