Article-Preprint

EODECAs, merging machine learning with dynamical systems, enhance interpretability and transparency in neural networks. They employ continuous ordinary differential equations, offering both high classification accuracy and an understanding of data processes, addressing the opacity of traditional deep learning models. This approach signifies a step towards more comprehensible machine learning models.

This paper analyzes the effectiveness of containment measures for SARS-CoV-2, using mobility data to gauge their impact. A deep learning model predicts virus spread scenarios in Italy, showing how these measures help flatten the infection curve and estimating the time required for their noticeable effects.