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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="research-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Discrete and Continuous Models and Applied Computational Science</journal-id><journal-title-group><journal-title xml:lang="en">Discrete and Continuous Models and Applied Computational Science</journal-title><trans-title-group xml:lang="ru"><trans-title>Discrete and Continuous Models and Applied Computational Science</trans-title></trans-title-group></journal-title-group><issn publication-format="print">2658-4670</issn><issn publication-format="electronic">2658-7149</issn><publisher><publisher-name xml:lang="en">Peoples' Friendship University of Russia named after Patrice Lumumba (RUDN University)</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">35922</article-id><article-id pub-id-type="doi">10.22363/2658-4670-2023-31-3-260-272</article-id><article-id pub-id-type="edn">KIJKGU</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>Articles</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>Статьи</subject></subj-group><subj-group subj-group-type="article-type"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Identification of COVID-19 spread factors in Europe based on causal analysis of medical interventions and socio-economic data</article-title><trans-title-group xml:lang="ru"><trans-title>Выявление факторов распространения COVID-19 в Европе на основе причинно-следственного анализа медицинских вмешательств и социально-экономических данных</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-1996-577X</contrib-id><name-alternatives><name xml:lang="en"><surname>Brou</surname><given-names>Kouame A.</given-names></name><name xml:lang="ru"><surname>Бру</surname><given-names>К. А.</given-names></name></name-alternatives><bio xml:lang="en"><p>PhD student of Information Technology Department</p></bio><email>broureino@gmail.com</email><xref ref-type="aff" rid="aff1"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Peoples’ Friendship University of Russia (RUDN University)</institution></aff><aff><institution xml:lang="ru">Российский университет дружбы народов</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2023-09-12" publication-format="electronic"><day>12</day><month>09</month><year>2023</year></pub-date><volume>31</volume><issue>3</issue><issue-title xml:lang="en">VOL 31, NO3 (2023)</issue-title><issue-title xml:lang="ru">ТОМ 31, №3 (2023)</issue-title><fpage>260</fpage><lpage>272</lpage><history><date date-type="received" iso-8601-date="2023-09-12"><day>12</day><month>09</month><year>2023</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2023, Brou K.A.</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2023, Бру К.А.</copyright-statement><copyright-year>2023</copyright-year><copyright-holder xml:lang="en">Brou K.A.</copyright-holder><copyright-holder xml:lang="ru">Бру К.А.</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">https://creativecommons.org/licenses/by-nc/4.0</ali:license_ref></license></permissions><self-uri xlink:href="https://journals.rudn.ru/miph/article/view/35922">https://journals.rudn.ru/miph/article/view/35922</self-uri><abstract xml:lang="en"><p style="text-align: justify;">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.</p></abstract><trans-abstract xml:lang="ru"><p style="text-align: justify;">С момента появления COVID-19 было получено огромное количество данных, помогающих понять, как развивался и распространялся вирус. Анализ таких данных помогает получить новые знания, необходимые для контроля за развитием эпидемии и предоставить лицам, принимающим решения, инструменты для принятия эффективных мер по сдерживанию эпидемии и минимизации социальных последствий. Анализу влияния медицинских методов лечения и социально-экономических факторов на передачу коронавируса было уделено много внимания. В этой работе мы применяем панельное авторегрессионное моделирование с распределённым запаздыванием (ARDL) к данным Европейского союза для выявления факторов распространения COVID-19 в Европе. Наш анализ показал, что немедикаментозные меры были успешными в снижении смертности, а строгость изоляции, политика тестирования на вирус и механизмы защиты пожилых людей оказывают положительное влияние на сдерживание эпидемии. Результаты панельных тестов попарной причинноследственной связи Думитреску-Херлина показывают, что для всех стран Евросоюза существует двунаправленная причинно-следственная связь между новыми смертями и факторами фармакологического вмешательства и что, с другой стороны, некоторые социально-экономические факторы вызывают новые смерти, когда обратное неверно.</p></trans-abstract><kwd-group xml:lang="en"><kwd>causality analysis</kwd><kwd>COVID-19</kwd><kwd>socio-economic</kwd><kwd>Dumitrescu-Hurlin’ panel</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>анализ причинно-следственных связей</kwd><kwd>COVID-19</kwd><kwd>социально-экономические факторы</kwd><kwd>группа Думитреску-Херлина</kwd></kwd-group><funding-group/></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>S. L. Priyadarsini and M. 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