Utilizing Graph Theory Algorithms for the Modeling and Analysis of COVID-19 Infection Dynamics

Abstract

The COVID-19 outbreak has shown how urgently good models are needed to understand and project the dissemination of infectious diseases. Graph theory's excellent basis for presenting and analyzing complex networks helps one to grasp COVID-19 transmission dynamics. This work investigates the simulation and evaluation of the COVID-19 spread using multiple graph theory approaches. We utilize algorithms, including centrality assessments, community detection, and epidemic spreading models, to identify significant transmission channels and viable intervention sites; we also study the usage of network-building strategies to show relationships between individuals and communities. By incorporating real-world data with graph-based models, we demonstrate how these approaches could increase the accuracy of infection estimates and direct public health strategies. Moreover, the paper discusses the advantages and limitations of graph theory approaches within the framework of pandemic modeling, therefore leading the next direction of investigation to improve disease outbreak reactions. This work highlights the important contribution of graph theory to increase our understanding of COVID-19 dissemination and offers a scientific foundation for handling associated problems in upcoming public health-catastrophes.