Analysis of COVID-19 evolution based on testing closeness of sequential data
Tomoko Matsui, Nourddine Azzaoui, Daisuke Murakami
TL;DR
This paper presents a practical algorithm for analyzing the evolution of COVID-19 by testing the closeness of sequential data, combining closeness testing with Markov chain algorithms to understand temporal changes.
Contribution
It introduces a novel algorithm that integrates closeness testing with Markov chain methods for analyzing sequential data, specifically applied to COVID-19 evolution.
Findings
Effective analysis of COVID-19 data over time
Identification of significant changes in disease progression
Demonstrated applicability of the algorithm to real-world data
Abstract
A practical algorithm has been developed for closeness analysis of sequential data that combines closeness testing with algorithms based on the Markov chain tester. It was applied to reported sequential data for COVID-19 to analyze the evolution of COVID-19 during a certain time period (week, month, etc.).
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Advanced Clustering Algorithms Research