Topological Data Analysis of COVID-19 Using Artificial Intelligence and Machine Learning Techniques in Big Datasets of Hausdorff Spaces
Allan Onyango, Benard Okelo, Richard Omollo
Abstract
In this paper, we carry out an in-depth topological data analysis (TDA) of COVID-19 pandemic using artificial intelligence (AI) and Machine Learning (ML) techniques. We show the distribution patterns of pandemic all over the world when it was at its peak with respect to big data sets in Hausdorff spaces. The results show that the world areas which experience a lot of cold seasons were affected most. Received: 31 January 2023 | Revised: 6 March 2023 | Accepted: 21 March 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work.
Topics & Concepts
Hausdorff spaceCoronavirus disease 2019 (COVID-19)Big dataTopological data analysisPandemicArtificial intelligenceHausdorff distanceComputer scienceSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Topology (electrical circuits)MathematicsData miningAlgorithmDiscrete mathematicsCombinatoricsInfectious disease (medical specialty)DiseasePathologyMedicineTopological and Geometric Data AnalysisBioinformatics and Genomic NetworksDigital Image Processing Techniques