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Introduction to the special issue on Data Science for COVID-19

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  • Ricardo Cao
  • José E. Chacón

Abstract

An introduction to this Special Issue on Data Science for COVID-19 is included in this paper. It contains a general overview about methods and applications of nonparametric inference and other flexible data science methods for the COVID-19 pandemic. Specifically, some methods existing before the COVID-19 outbreak are surveyed, followed by an account of survival analysis methods for COVID-related times. Then, several nonparametric tools for the estimation of certain COVID rates are revised, along with the forecasting of most relevant series counts, and some other related problems. Within this setup, the papers published in this special issue are briefly commented in this introductory article.

Suggested Citation

  • Ricardo Cao & José E. Chacón, 2022. "Introduction to the special issue on Data Science for COVID-19," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 34(3), pages 555-569, July.
    • Handle: RePEc:taf:gnstxx:v:34:y:2022:i:3:p:555-569
    DOI: 10.1080/10485252.2022.2108288
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