2021/04/14 by Andrés L. Suárez-Cetrulo, Andrés L. Suárez‐Cetrulo, Ankit Kumar +5 · 3 citations
Computer Science · Mathematics · #Anomaly Detection Techniques and Applications #Artificial intelligence #COVID-19 epidemiological studies #Computer science #Coronavirus disease 2019 (COVID-19) #Data Stream Mining Techniques #Disease #FOS: Computer and information sciences #Infectious disease (medical specialty) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine learning #Pandemic #Scheme (mathematics) #Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2104.09325
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2021/04/14 · arxiv created 2021/04/21 · arxiv updated 2021/04/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.