2019/11/13 by José L. Herrera-Diestra, José Luís Herrera, Javier M. Buldú +2
Biochemistry, Genetics and Molecular Biology · Computer Science · Economics, Econometrics and Finance · Physics and Astronomy · #Biology #Complex Systems and Time Series Analysis #Complex network #Computer science #Data mining #Entropy (arrow of time) #Fractal and DNA sequence analysis #Machine learning #Series (stratigraphy) #Theoretical computer science #Time Series Analysis and Forecasting #Time series #physics.bio-ph #physics.data-an #physics.soc-ph
paper · pdf · doi:10.1098/rspa.2019.0777
20 pages, 10 figures
arxiv created 2019/11/13 · openalex publication_date 2020/04/01 · arxiv updated 2021/03/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
We introduce a new methodology, which is based on the construction of epidemic networks, to analyse the evolution of epidemic time series. First, we translate the time series into ordinal patterns containing information about local fluctuations in disease prevalence. Each pattern is associated with a node of a network, whose (directed) connections arise from consecutive appearances in the series. The analysis of the network structure and the role of each pattern allows them to be classified according to the enhancement of entropy/complexity along the series, giving a different point of view about the evolution of a given disease.