Functional data analysis: Application to daily observation of COVID-19 prevalence in France

Kayode Oshinubi; Firas Ibrahim; Mustapha Rachdi; Jacques Demongeot; Kayode Oshinubi; Firas Ibrahim; Mustapha Rachdi; Jacques Demongeot

doi:10.3934/math.2022298

AIMS Mathematics

2022, Volume 7, Issue 4: 5347-5385. doi: 10.3934/math.2022298

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Functional data analysis: Application to daily observation of COVID-19 prevalence in France

Laboratory AGEIS EA 7407, Team Tools for e-Gnosis Medical & Labcom CNRS/UGA/OrangeLabs Telecom4Health, Faculty of Medicine, University Grenoble Alpes (UGA), 38700 La Tronche, France

Received: 15 November 2021 Revised: 20 December 2021 Accepted: 26 December 2021 Published: 06 January 2022
MSC : 62R10, 62P10

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.
- canonical correlation,
- clustering,
- COVID-19,
- prediction,
- functional data analysis,
- functional principal components analysis,
- functional linear regression
Citation: Kayode Oshinubi, Firas Ibrahim, Mustapha Rachdi, Jacques Demongeot. Functional data analysis: Application to daily observation of COVID-19 prevalence in France[J]. AIMS Mathematics, 2022, 7(4): 5347-5385. doi: 10.3934/math.2022298

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Abstract

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.

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