Modelling the COVID-19 Virus Evolution With Incremental Machine Learning

The investment of time and resources for better strategies and methodologies to tackle a potential pandemic is key for dealing with potential outbreaks of new variants or other viruses in the future. In this work, we recreated the scene of 2020 for the fifty countries with more COVID-19 cases reported. We performed some experiments to compare state-of-the-art machine learning algorithms, such as LSTM, against online incremental learning methods (ILMs) in terms of how well they adapted to the daily changes in the spread of the disease and predict future COVID-19 cases. To compare the methods, we performed two experiments: In the first experiment, we trained the models using only data from the country we predicted. In the second one, we used data from the fifty countries to train and predict each one of them. In these two experiments, we used a static hold-out approach for all the methods. Results show that ILMs are a promising approach to model the disease changes over time; ILMs are always up-to-date with the latest state of the data distribution, and they have a significantly lower computational cost than other techniques such as LSTMs.