Two machine-learning approaches for short-term COVID-19 hospitalization forecasting in Slovakia

COVID-19 is a life-threatening novel respiratory virus-borne disease, which was discovered in December 2019 in Wuhan and subsequently spread globally. Monitoring and predicting COVID-19 epidemic data is crucial to control pandemic outbreaks. Machine learning-based methods, including deep learning, are promising approaches to predict COVID-19 data such as new cases, infected patients, and deaths. Our study focused on short-term COVID-19 hospitalizations forecasting using two machine learning approaches— ensemble time-series method and multilayer perceptron (MLP) feedforward network method. Both methods make predictions based on hospitalization, polymerase chain reaction (PCR), and antigen (Ag) test data, which were collected between October 2020 and June 2021 in Slovakia for our study. The ensemble time-series method was more sensitive in the beginning of experimental period but failed when the number of hospitalizations began to drop. The MLP method was ineffective in the beginning because of lack of training data but improved when more robust data was available;this method is promising for monitoring the third wave of pandemic in Slovakia. Copyright © 2021 for this paper by its authors.