Modeling the evolution of deaths from infectious diseases with functional data models: The case of COVID-19 in Brazil

Julian A.A. Collazos, Ronaldo Dias, Marcelo C. Medeiros

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we apply statistical methods for functional data to explore the heterogeneity in the registered number of deaths of COVID-19, over time. The cumulative daily number of deaths in regions across Brazil is treated as continuous curves (functional data). The first stage of the analysis applies clustering methods for functional data to identify and describe potential heterogeneity in the curves and their functional derivatives. The estimated clusters are labeled with different “levels of alert” to identify cities in a possible critical situation. In the second stage of the analysis, we apply a functional quantile regression model for the death curves to explore the associations with functional rates of vaccination and stringency and also with several scalar geographical, socioeconomic and demographic covariates. The proposed model gave a better curve fit at different levels of the cumulative number of deaths when compared to a functional regression model based on ordinary least squares. Our results add to the understanding of the development of COVID-19 death counts.

Original languageEnglish (US)
Pages (from-to)993-1012
Number of pages20
JournalStatistics in Medicine
Volume42
Issue number7
DOIs
StatePublished - Mar 30 2023
Externally publishedYes

Keywords

  • B-splines basis functions
  • COVID-19
  • functional data analysis
  • functional quantile regression for functional partially linear model
  • heterogeneity

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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