Graph Theory Approach for the Control of COVID-19 Diffusion

Pandemics often arise from the diffusion of infectious diseases over large regions, spanning multiple continents. Effective non-pharmaceutical interventions (NPIs) at the onset of an outbreak can significantly curb the spread. However, the complexity of human interactions can hinder the strict and timely imposition of such measures. Given the extensive damage caused by COVID-19, it is imperative to develop computational strategies that control and prevent the global spread of infectious diseases. This study proposes a model of COVID-19 spread networks, formulated based on daily confirmed cases per country, with mutual information as a measure of interdependence between countries. Utilizing graph theory, this approach identifies key nodes (countries) that play influential roles in the COVID-19 spread network. A control framework based on network theory is then introduced to mitigate and potentially eradicate further infection spread. Results demonstrate that the proposed framework holds substantial promise in preventing the transmission of COVID-19 and similar outbreaks when implemented promptly.