Functional data analysis: Application to daily observation of COVID-19 prevalence in France
AIMS Mathematics
; 7(4):5347-5385, 2022.
Article
in English
| Scopus | ID: covidwho-1626405
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. © 2022 the Author(s), licensee AIMS Press.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Observational study
/
Prognostic study
Language:
English
Journal:
AIMS Mathematics
Year:
2022
Document Type:
Article
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