A novel honey badger algorithm with multilayer perceptron for predicting COVID-19 time series data.

The COVID-19 pandemic has affected the health, economy, and all aspects of human lives around the world. Accurate prediction of the daily new cases of COVID-19 is critical for precise programming and taking needed measures to prevent the outbreak of it. Hence, in the present study, a new hybrid intelligent model is developed by hybridizing the artificial neural network (ANN) with the honey badger algorithm (HBA) to accurately predict the daily new cases of COVID-19 in Brazil, India, Russia, and the US. The performance of the hybrid model, namely HBA-ANN, was compared with the stand-alone ANN and gene expression programming (GEP) model using statistical criteria, such as correlation coefficient (R), root mean square error, scatter index (SI), and Nash–Sutcliffe efficiency (NSE), and graphical criteria, such as Taylor diagram, scatter plot, and box plot. According to the Taylor diagram, in each data series, the predicted values with the HBA-ANN model have the lowest distance from the observation points, indicating the high accuracy of the HBA-ANN model compared to the ANN and GEP models. Furthermore, the HBA-ANN model has high values for R (0.999, 0.899, 0.853, and 0.993 for Brazil, Russia, India, and the US, respectively) and could reduce the errors of the ANN model by 97.22, 43.68, 85.41, and 93.83% for Brazil, Russia, India, and the US, respectively. For each data series, the SI for the HBA-ANN model is less than 0.1, and the NSE is between 0 and 1, indicating a very good performance in predicting the COVID-19 cases with the developed model. Based on the superior performance of the HBA-ANN model, it is recommended to implement the HBA algorithm to increase the prediction accuracy of the models in medicine and other scientific fields. Moreover, it is suggested to investigate the accuracy of the hybridized HBA algorithm with other artificial intelligence (AI) models. [ABSTRACT FROM AUTHOR]