An Improved Type-1 Fuzzy Regression Function for COVID-19 Cases Prediction

Abstract

The deficiency in data collection and reporting has led to the emergence of uncertainty in the data in the pandemic process. These matters cause that traditional statistical and mathematical models have been functionless and unreliable in this issue. In this respect, this study presents an ensemble prediction model with innovative and contemporary properties to model COVID-19 cases in the UK and USA inclusively throughout the whole pandemic process. The proposed ensemble prediction model is composed of an assembly of Type-1 fuzzy regression functions with elastic net regularization (E-T1FRF) and radial basis function neural networks (RBFNNs). Thus, the proposed ensemble model can successfully model the uncertainty in the data with the fuzzy modelling perspective of T1FRF and also thoroughly adapt to the patterns in the data thanks to the flexible modelling ability of RBFNN based on data. With the proposed ensemble prediction model, the cases in the entire pandemic process from the beginning of March 2020 to the end of June 2022 were modelled and predicted for 23 different periods in one-month prediction steps. The proposed model produced predictions with MAPE values below 3% in all but periods except for three periods. Also, the average MAPE values for all periods were obtained at around 2% for the UK and only 1.5% for the US. These results, at a reasonable level of error, demonstrated the practicality of the usage of the proposed community model for other countries and provided valuable information for future action.