The investment of time and resources for better strategies and methodologies to tackle a potential pandemic is key to deal with potential outbreaks of new variants or other viruses in the future . In this work, we recreated the scene of a year ago , 2020, when the pandemic erupted across the world for the fifty countries with more COVID-19 cases reported . We performed some experiments in which we compare state-of-the-art machine learning algorithms, such as LSTM, against online incremental machine learning algorithms to adapt them to the daily changes in the spread of the disease and predict future COVID-19 cases . To compare the methods, we performed three experiments: In the first one, we trained the models using only data from the country we predicted . In the second one, we use data from all fifty countries to train and predict each of them . In the first and second experiment, we used a static hold-out approach for all methods . In the third experiment, we trained the incremental methods sequentially, using a prequential evaluation . This scheme is not suitable for most state-of-the-art machine learning algorithms because they need to be retrained from scratch for every batch of predictions, causing a computational burden . Results show that incremental methods are a promising approach to adapt to changes of the disease over time; they are always up to date with the last state of the data distribution, and they have a significantly lower computational cost than other techniques such as LSTMs.