Abstract

The coronavirus COVID-19 is affecting around the world with strong differences between countries and regions. Extensive datasets are available for visual inspection and downloading. The material has limitations for phenomenological modeling but data-based methodologies can be used. This research focuses on the intelligent temporal analysis of datasets in developing compact solutions for early detection of levels, trends, episodes, and severity of situations. The methodology has been tested in the analysis of daily new confirmed COVID-19 cases and deaths in six countries. The datasets are studied per million people to get comparable indicators. Nonlinear scaling brings the data of different countries to the same scale, and the temporal analysis is based on the scaled values. The same approach can be used for any country or a group of people, e.g., hospital patients, patients in intensive care, or people in different age categories. During the pandemic, the scaling functions expanded for the confirmed cases but remained practically unchanged for the confirmed deaths, which is consistent with increasing testing.