Modeling of COVID-19 Pandemic vis-à-vis Some Socio-Economic Factors
The impact of the COVID-19 epidemic on the socio-economic status of countries around the world should not be underestimated, when we consider the role it has played in various countries. Many people were unemployed, many households were careful about their spending, and a greater social divide in th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-01-01
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| Series: | Frontiers in Applied Mathematics and Statistics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fams.2021.786983/full |
| _version_ | 1828146072100274176 |
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| author | Kayode Oshinubi Mustapha Rachdi Jacques Demongeot |
| author_facet | Kayode Oshinubi Mustapha Rachdi Jacques Demongeot |
| author_sort | Kayode Oshinubi |
| collection | DOAJ |
| description | The impact of the COVID-19 epidemic on the socio-economic status of countries around the world should not be underestimated, when we consider the role it has played in various countries. Many people were unemployed, many households were careful about their spending, and a greater social divide in the population emerged in 14 different countries from the Organization for Economic Co-operation and Development (OECD) and from Africa (that is, in developed and developing countries) for which we have considered the epidemiological data on the spread of infection during the first and second waves, as well as their socio-economic data. We established a mathematical relationship between Theil and Gini indices, then we investigated the relationship between epidemiological data and socio-economic determinants, using several machine learning and deep learning methods. High correlations were observed between some of the socio-economic and epidemiological parameters and we predicted three of the socio-economic variables in order to validate our results. These results show a clear difference between the first and the second wave of the pandemic, confirming the impact of the real dynamics of the epidemic’s spread in several countries and the means by which it was mitigated. |
| first_indexed | 2024-04-11T20:41:45Z |
| format | Article |
| id | doaj.art-7be9057d4b5a4bfe9cc28a0691a6b834 |
| institution | Directory Open Access Journal |
| issn | 2297-4687 |
| language | English |
| last_indexed | 2024-04-11T20:41:45Z |
| publishDate | 2022-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Applied Mathematics and Statistics |
| spelling | doaj.art-7be9057d4b5a4bfe9cc28a0691a6b8342022-12-22T04:04:11ZengFrontiers Media S.A.Frontiers in Applied Mathematics and Statistics2297-46872022-01-01710.3389/fams.2021.786983786983Modeling of COVID-19 Pandemic vis-à-vis Some Socio-Economic FactorsKayode OshinubiMustapha RachdiJacques DemongeotThe impact of the COVID-19 epidemic on the socio-economic status of countries around the world should not be underestimated, when we consider the role it has played in various countries. Many people were unemployed, many households were careful about their spending, and a greater social divide in the population emerged in 14 different countries from the Organization for Economic Co-operation and Development (OECD) and from Africa (that is, in developed and developing countries) for which we have considered the epidemiological data on the spread of infection during the first and second waves, as well as their socio-economic data. We established a mathematical relationship between Theil and Gini indices, then we investigated the relationship between epidemiological data and socio-economic determinants, using several machine learning and deep learning methods. High correlations were observed between some of the socio-economic and epidemiological parameters and we predicted three of the socio-economic variables in order to validate our results. These results show a clear difference between the first and the second wave of the pandemic, confirming the impact of the real dynamics of the epidemic’s spread in several countries and the means by which it was mitigated.https://www.frontiersin.org/articles/10.3389/fams.2021.786983/fullCOVID-19regressionsocio-economic factorsmachine learningdata analysis |
| spellingShingle | Kayode Oshinubi Mustapha Rachdi Jacques Demongeot Modeling of COVID-19 Pandemic vis-à-vis Some Socio-Economic Factors Frontiers in Applied Mathematics and Statistics COVID-19 regression socio-economic factors machine learning data analysis |
| title | Modeling of COVID-19 Pandemic vis-à-vis Some Socio-Economic Factors |
| title_full | Modeling of COVID-19 Pandemic vis-à-vis Some Socio-Economic Factors |
| title_fullStr | Modeling of COVID-19 Pandemic vis-à-vis Some Socio-Economic Factors |
| title_full_unstemmed | Modeling of COVID-19 Pandemic vis-à-vis Some Socio-Economic Factors |
| title_short | Modeling of COVID-19 Pandemic vis-à-vis Some Socio-Economic Factors |
| title_sort | modeling of covid 19 pandemic vis a vis some socio economic factors |
| topic | COVID-19 regression socio-economic factors machine learning data analysis |
| url | https://www.frontiersin.org/articles/10.3389/fams.2021.786983/full |
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