Complex Network Approaches for Epidemic Modeling: A Case Study of COVID-19

Since the SARS-CoV-2 outbreak, the importance of mathematical modeling as a tool for comprehending disease dynamics has been highlighted, with several mathematical modeling techniques being applied and developed to simulate and measure the impact of interventions aimed at controlling the spread of the disease and minimizing its burden. In this work, we applied complex network techniques to analyze a Susceptible-Exposed-Asymptomatic-Hospitalized-Recovered (SEAHR) model to describe COVID-19 transmission dynamics, using the Basque Country region of Spain as a case study. We compared two network modeling approaches: the Watts-Strogatz network and the Barabasi-Albert scale-free network. By applying immunization strategies on both networks, we demonstrate that targeted immunization yields superior results within a scale-free network due to its increased heterogeneity. Moreover, the basic reproduction number of the model is calculated, and sensitivity analysis is performed to determine the influence of the model parameters on the disease dynamics.