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Analysis of COVID-19 evolution based on testing closeness of sequential data.

Tomoko MatsuiNourddine AzzaouiDaisuke Murakami
Published in: Japanese journal of statistics and data science (2022)
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.).
Keyphrases
  • coronavirus disease
  • sars cov
  • electronic health record
  • machine learning
  • big data
  • randomized controlled trial
  • clinical trial
  • double blind