A graph-based methodology for tracking covid-19 in time series datasets

Abstract

Since its first appearance in December 2019, Covid-19 has become a wide field of scientific research. Starting from biology to bioinformatic solutions, Artificial Intelligence has contributed in turn as a powerful tool for tracking and predicting the outbreak of Covid-19 using different types of datasets. Chest X-ray images are widely used in computer vision applications and Time series datasets are used for predicting the spread of the novel coronavirus. Graph analytics is a recent field of study that links the mathematical definition and operations of a graph to its application in computer science as a complex data structure, this combination has played a critical role in making graph-based applications present in different fields. One of the most powerful graph analytics is community detection which is an intelligent and unsupervised grouping of a set of graph structured data using the similarity between them. The aim of this work is to highlight the importance of graph-based algorithms in tracking Covid-19 using time series datasets, our work will also focus on Spectral Clustering (SC) as a community detection approach to extract clusters from the input datasets. Further applications are needed in order to validate the proposed theoretical approach.