Open AccessFunctional data analysis: Application to daily observation of COVID-19 prevalence in France
Author(s) -
Kayode Oshinubi,
AUTHOR_ID,
Firas Ibrahim,
Mustapha Rachdi,
Jacques Demongeot
Publication year - 2022
Publication title -
aims mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.329
H-Index - 15
ISSN - 2473-6988
DOI - 10.3934/math.2022298
Subject(s) - functional data analysis , functional principal component analysis , covid-19 , statistics , smoothing , cluster analysis , data set , outbreak , regression analysis , principal component analysis , medicine , geography , econometrics , computer science , mathematics , virology , disease , infectious disease (medical specialty)
In this paper we use the technique of functional data analysis to model daily hospitalized, deceased, Intensive Care Unit (ICU) cases and return home patient numbers along the COVID-19 outbreak, considered as functional data across different departments in France while our response variables are numbers of vaccinations, deaths, infected, recovered and tests in France. These sets of data were considered before and after vaccination started in France. After smoothing our data set, analysis based on functional principal components method was performed. Then, a clustering using k-means techniques was done to understand the dynamics of the pandemic in different French departments according to their geographical location on France map. We also performed canonical correlations analysis between variables. Finally, we made some predictions to assess the accuracy of the method using functional linear regression models.