Analysis of Covid-19 Transmission Using Complex Networks

The global public health crisis caused by the SARS-CoV-2-19 (COVID-19) pandemic has highlighted the need for research into contagion complexity. This challenge necessitates the development and testing of various approaches to manage rapidly changing information with high impact. In this paper, we employ time series analysis and complex networks analysis to compare the evolution, spread, and containment of COVID-19 pandemics in eleven countries and globally. Our analysis enables us to observe the dynamics of spread and the impact of different strategies employed by each country in increasing and decreasing cases through complex network techniques. Additionally, we explore the transformation of data behavior over time as our understanding of the virus improves. Our findings provide important insights into the limitations of using statistical models and suggest that simulation of new cases of COVID-19 data can be modeled using complex networks. The complex network model provides a general description of contagion dynamics in the 11 countries and worldwide situation. This paper contributes by highlighting the limitations of using statistical models to infer and study early time series data and proposing the use of a complex network approach to study contagion dynamics.