Identification of COVID-19 spread factors in Europe based on causal analysis of medical interventions and socio-economic data
Since the appearance of COVID-19, a huge amount of data has been obtained to help understand how the virus evolved and spread. The analysis of such data can provide new insights which are needed to control the progress of the epidemic and provide decision-makers with the tools to take effective meas...
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| Format: | Article |
| Language: | English |
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Peoples’ Friendship University of Russia (RUDN University)
2023-09-01
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| Series: | Discrete and Continuous Models and Applied Computational Science |
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| Online Access: | https://journals.rudn.ru/miph/article/viewFile/35922/22464 |
| _version_ | 1797682223645720576 |
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| author | Kouame A. Brou |
| author_facet | Kouame A. Brou |
| author_sort | Kouame A. Brou |
| collection | DOAJ |
| description | Since the appearance of COVID-19, a huge amount of data has been obtained to help understand how the virus evolved and spread. The analysis of such data can provide new insights which are needed to control the progress of the epidemic and provide decision-makers with the tools to take effective measures to contain the epidemic and minimize the social consequences. Analysing the impact of medical treatments and socioeconomic factors on coronavirus transmission has been given considerable attention. In this work, we apply panel autoregressive distributed lag modelling (ARDL) to European Union data to identify COVID-19 transmission factors in Europe. Our analysis showed that non-medicinal measures were successful in reducing mortality, while strict isolation virus testing policies and protection mechanisms for the elderly have had a positive effect in containing the epidemic. Results of Dumitrescu-Hurlin paired-cause tests show that a bidirectional causal relationship exists for all EU countries causal relationship between new deaths and pharmacological interventions factors and that, on the other hand, some socioeconomic factors cause new deaths when the reverse is not true. |
| first_indexed | 2024-03-11T23:56:26Z |
| format | Article |
| id | doaj.art-d6bdf6010cce4a39ae8f4c3741e5f5b8 |
| institution | Directory Open Access Journal |
| issn | 2658-4670 2658-7149 |
| language | English |
| last_indexed | 2024-03-11T23:56:26Z |
| publishDate | 2023-09-01 |
| publisher | Peoples’ Friendship University of Russia (RUDN University) |
| record_format | Article |
| series | Discrete and Continuous Models and Applied Computational Science |
| spelling | doaj.art-d6bdf6010cce4a39ae8f4c3741e5f5b82023-09-18T12:29:16ZengPeoples’ Friendship University of Russia (RUDN University)Discrete and Continuous Models and Applied Computational Science2658-46702658-71492023-09-0131326027210.22363/2658-4670-2023-31-3-260-27221024Identification of COVID-19 spread factors in Europe based on causal analysis of medical interventions and socio-economic dataKouame A. Brou0https://orcid.org/0000-0003-1996-577XPeoples’ Friendship University of Russia (RUDN University)Since the appearance of COVID-19, a huge amount of data has been obtained to help understand how the virus evolved and spread. The analysis of such data can provide new insights which are needed to control the progress of the epidemic and provide decision-makers with the tools to take effective measures to contain the epidemic and minimize the social consequences. Analysing the impact of medical treatments and socioeconomic factors on coronavirus transmission has been given considerable attention. In this work, we apply panel autoregressive distributed lag modelling (ARDL) to European Union data to identify COVID-19 transmission factors in Europe. Our analysis showed that non-medicinal measures were successful in reducing mortality, while strict isolation virus testing policies and protection mechanisms for the elderly have had a positive effect in containing the epidemic. Results of Dumitrescu-Hurlin paired-cause tests show that a bidirectional causal relationship exists for all EU countries causal relationship between new deaths and pharmacological interventions factors and that, on the other hand, some socioeconomic factors cause new deaths when the reverse is not true.https://journals.rudn.ru/miph/article/viewFile/35922/22464causality analysiscovid-19socio-economicdumitrescu-hurlin’ panel |
| spellingShingle | Kouame A. Brou Identification of COVID-19 spread factors in Europe based on causal analysis of medical interventions and socio-economic data Discrete and Continuous Models and Applied Computational Science causality analysis covid-19 socio-economic dumitrescu-hurlin’ panel |
| title | Identification of COVID-19 spread factors in Europe based on causal analysis of medical interventions and socio-economic data |
| title_full | Identification of COVID-19 spread factors in Europe based on causal analysis of medical interventions and socio-economic data |
| title_fullStr | Identification of COVID-19 spread factors in Europe based on causal analysis of medical interventions and socio-economic data |
| title_full_unstemmed | Identification of COVID-19 spread factors in Europe based on causal analysis of medical interventions and socio-economic data |
| title_short | Identification of COVID-19 spread factors in Europe based on causal analysis of medical interventions and socio-economic data |
| title_sort | identification of covid 19 spread factors in europe based on causal analysis of medical interventions and socio economic data |
| topic | causality analysis covid-19 socio-economic dumitrescu-hurlin’ panel |
| url | https://journals.rudn.ru/miph/article/viewFile/35922/22464 |
| work_keys_str_mv | AT kouameabrou identificationofcovid19spreadfactorsineuropebasedoncausalanalysisofmedicalinterventionsandsocioeconomicdata |