Exploring the Effectiveness of Graph-based Computational Models in COVID-19 Research

The world has witnessed various scientific disciplines’ rapid growth and advancement, leading to groundbreaking discoveries and advances in multiple fields in recent years. One such field that has gained significant attention, particularly during the COVID-19 pandemic, is the application of graph theory techniques in studying the spread and mitigation of the virus. In this paper, we delve into the intricacies of graph theory and its utilization in analyzing COVID-19, shedding light on the innovative approaches researchers worldwide employ. Also, the study evaluates the various implementation of graph theories in spreading and controlling the virus using diverse datasets. The researchers retrieved several works in the COVID-19 and graph theory field from digital databases. However, studies deducted that GT approaches, algorithms and techniques offer insights into transmission hotspots, spread dynamics in social, control and mobility networking, vaccination optimization, evaluation of interventions and epidemic prediction, among other valuable findings. Limitations and future directions were also directed in the study.