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Analysis of COVID-19 evolution based on testing closeness of sequential data
Tomoko Matsui1, Nourddine Azzaoui2, Daisuke Murakami1
1The Institute of Statistical Mathematics, Tachikawa, Japan.
Japanese Journal of Statistics and Data Science
|April 15, 2022
Summary
A new algorithm analyzes sequential data for closeness, combining closeness testing and Markov chain methods. This approach was used to study COVID-19 data evolution over time.
Area of Science:
- Computational Biology
- Epidemiology
- Data Science
Background:
- Sequential data analysis is crucial for understanding dynamic processes.
- Markov chain models are effective for analyzing state transitions.
- Understanding disease evolution requires robust analytical tools.
Purpose of the Study:
- To develop a practical algorithm for closeness analysis of sequential data.
- To apply the algorithm to COVID-19 data for evolutionary analysis.
- To analyze temporal trends in COVID-19 spread.
Main Methods:
- Developed a novel algorithm integrating closeness testing with Markov chain principles.
- Applied the algorithm to sequential COVID-19 epidemiological data.
- Analyzed data across various time scales (weekly, monthly).
Main Results:
- The algorithm provides a practical method for closeness analysis.
- Successfully applied to COVID-19 data, revealing evolutionary patterns.
- Demonstrated utility in analyzing disease progression over specific periods.
Conclusions:
- The combined algorithm offers an effective approach for sequential data analysis.
- This method enhances the understanding of infectious disease dynamics.
- Applicable for analyzing the evolution of epidemics like COVID-19.
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