Using symbolic networks to analyse dynamical properties of disease outbreaks

TL;DR

This paper presents a novel network-based method to analyze epidemic time series by translating disease prevalence fluctuations into ordinal patterns and constructing networks to study their evolution and complexity.

Contribution

The authors introduce a new symbolic network approach to analyze disease outbreak dynamics, offering a fresh perspective on epidemic evolution.

Findings

Network structure reveals disease progression patterns

Ordinal patterns help classify epidemic stages

Entropy and complexity measures track outbreak evolution

Abstract

We introduce a new methodology to analyze the evolution of epidemic time series, which is based on the construction of epidemic networks. First, we translate the time series into ordinal patterns containing information about local fluctuations of the disease prevalence. Each pattern is associated to 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 classifying them according to the enhancement of entropy/complexity along the series, giving a different point of view about the evolution of a given disease.