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<article article-type="research-article" dtd-version="1.3" 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" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">mireabulletin</journal-id><journal-title-group><journal-title xml:lang="ru">Russian Technological Journal</journal-title><trans-title-group xml:lang="en"><trans-title>Russian Technological Journal</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2782-3210</issn><issn pub-type="epub">2500-316X</issn><publisher><publisher-name>RTU MIREA</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.32362/2500-316X-2023-11-4-59-71</article-id><article-id custom-type="elpub" pub-id-type="custom">mireabulletin-735</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>МАТЕМАТИЧЕСКОЕ МОДЕЛИРОВАНИЕ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>MATHEMATICAL MODELING</subject></subj-group></article-categories><title-group><article-title>Моделирование пространственного распространения волн пандемии COVID-19 в России на основе кинетико-переносного описания</article-title><trans-title-group xml:lang="en"><trans-title>Modeling of spatial spread of COVID-19 pandemic waves in Russia using a kinetic-advection model</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-2568-3453</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Аристов</surname><given-names>В. В.</given-names></name><name name-style="western" xml:lang="en"><surname>Aristov</surname><given-names>V. V.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Аристов Владимир Владимирович, д.ф.-м.н., профессор, кафедра высшей математики Института искусственного интеллекта; главный научный сотрудник</p><p>119454, Москва, пр-т Вернадского, д. 78</p><p>119333, Москва, ул. Вавилова, д. 44/2</p><p>Scopus Author ID 35517535600</p></bio><bio xml:lang="en"><p>Vladimir V. Aristov, Dr. Sci. (Phys.-Math.), Professor, Department of Higher Mathematics, Institute of Artificial Intelligence; Chief Researcher, Federal Research Center “Computer Science and Control”</p><p>78, Vernadskogo pr., Moscow, 119454</p><p>44/2, Vavilova ul., Moscow, 119333</p><p> Scopus Author ID 35517535600</p></bio><email xlink:type="simple">aristovvl@yandex.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-6487-6093</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Строганов</surname><given-names>А. В.</given-names></name><name name-style="western" xml:lang="en"><surname>Stroganov</surname><given-names>A. V.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Строганов Андрей Валентинович, к.ф.-м.н., доцент, кафедра высшей математики Института искусственного интеллекта</p><p>119454, Москва, пр-т Вернадского, д. 78</p><p>Scopus Author ID 36667697700</p></bio><bio xml:lang="en"><p>Andrey V. Stroganov, Cand. Sci. (Phys.-Math.), Assistant Professor, Department of Higher Mathematics, Institute of Artificial Intelligence</p><p>78, Vernadskogo pr., Moscow, 119454</p><p>Scopus Author ID 36667697700</p></bio><email xlink:type="simple">savthe@gmail.com</email><xref ref-type="aff" rid="aff-2"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-0072-6226</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Ястребов</surname><given-names>А. Д.</given-names></name><name name-style="western" xml:lang="en"><surname>Yastrebov</surname><given-names>A. D.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Ястребов Андрей Дмитриевич, аспирант, кафедра высшей математики Института искусственного интеллекта</p><p>119454, Москва, пр-т Вернадского, д. 78</p><p>Scopus Author ID 57314418000</p></bio><bio xml:lang="en"><p>Andrey D. Yastrebov, Postgraduate Student, Department of Higher Mathematics, Institute of Artificial Intelligence</p><p>78, Vernadskogo pr., Moscow, 119454</p><p>Scopus Author ID 57314418000</p></bio><email xlink:type="simple">andr.yast711@gmail.com</email><xref ref-type="aff" rid="aff-2"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>МИРЭА – Российский технологический университет; Федеральный исследовательский центр «Информатика и управление» Российской академии наук</institution><country>Россия</country></aff><aff xml:lang="en"><institution>MIREA – Russian Technological University; Federal Research Center “Computer Science and Control”</institution><country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru"><institution>МИРЭА – Российский технологический университет</institution><country>Россия</country></aff><aff xml:lang="en"><institution>MIREA – Russian Technological University</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2023</year></pub-date><pub-date pub-type="epub"><day>01</day><month>08</month><year>2023</year></pub-date><volume>11</volume><issue>4</issue><fpage>59</fpage><lpage>71</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Аристов В.В., Строганов А.В., Ястребов А.Д., 2023</copyright-statement><copyright-year>2023</copyright-year><copyright-holder xml:lang="ru">Аристов В.В., Строганов А.В., Ястребов А.Д.</copyright-holder><copyright-holder xml:lang="en">Aristov V.V., Stroganov A.V., Yastrebov A.D.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://www.rtj-mirea.ru/jour/article/view/735">https://www.rtj-mirea.ru/jour/article/view/735</self-uri><abstract><sec><title>Цели</title><p>Цели. Пандемия COVID-19 обладает рядом важных особенностей по сравнению с прошлыми эпидемиями. Помимо высокой степени заражения, она имеет высокую скорость распространения за счет мобильности населения, связанной, в частности, с возросшей скоростью средств передвижения. Целью данной работы является построение математической модели распространения пандемии и выявление закономерностей в предположении, что основным источником вирусной инфекции в России является г. Москва. Для этого строится двухпараметрическая кинетическая модель, описывающая пространственное распространение эпидемии. Параметры находятся с помощью теоретических построений, оценок известных данных о статистике передвижения транспортных средств и плотности населения в различных странах, а также с учетом развития первой волны на примере России, Италии и Чили с проверкой значений для последующих эпидемических волн. Исследуется возможность предсказывать скорость пространственного распространения вируса по временно́му интервалу запаздывания достижения пика заражений в России по сравнению с Москвой. Это связано с географическими особенностями: в России, как и в некоторых других странах, можно выделить основной источник распространения инфекции. Таким источником в России выступает г. Москва – крупнейший в стране транспортный узел. Для реализации цели в настоящей работе изучается развитие эпидемических событий в России, начиная с 3-й, и вплоть до последних 5-й и 6-й волн.</p></sec><sec><title>Методы</title><p>Методы. Использованы методы математического моделирования и методы обработки статистических данных.</p></sec><sec><title>Результаты</title><p>Результаты. Подтверждено, что величина запаздывания достижения пика заражений составляет в среднем 2.5 недели. Выявлена сохраняемость параметров для различных волн, поэтому модель обладает предсказательными возможностями. Проверки проводились для последовательности волн, для которых делались соответствующие предсказания о развитии заражения для России в целом и о том, когда произойдет спад. Данные прогнозы подтвердились для всех волн, начиная с 3-й, и вплоть до последней 6-й волны, что подтверждает найденную закономерность, важную для прогнозирования будущих событий.</p></sec><sec><title>Выводы</title><p>Выводы. Прогнозы о начале и скорости выздоровления подтвердились, что дало возможность уверенно прогнозировать, в частности, протекание 5-й и 6-й волн пандемии, связанной с новым вирусным штаммом «омикрон». Предсказания, которые делались заранее, были проверены и получили подтверждение.</p></sec></abstract><trans-abstract xml:lang="en"><sec><title>Objectives</title><p>Objectives. COVID-19 has a number of specific characteristics that distinguish it from past pandemics. In addition to the high infection rate, the high spread rate is due to the increased mobility of contemporary populations. The aim of the present work is to construct a mathematical model for the spread of the pandemic and identify patterns under the assumption that Moscow comprises the main source of viral infection in Russia. For this purpose, a twoparameter kinetic model describing the spatial spread of the epidemic is developed. The parameters are determined using theoretical constructions alongside statistical vehicle movement and population density data from various countries, additionally taking into account the development of the first wave on the examples of Russia, Italy and Chile with verification of values obtained from subsequent epidemic waves. This paper studies the development of epidemic events in Russia, starting from the third and including the most recent fifth and sixth waves. Our twoparameter model is based on a kinetic equation. The investigated possibility of predicting the spatial spread of the virus according to the time lag of reaching the peak of infections in Russia as a whole as compared to Moscow is connected with geographical features: in Russia, as in some other countries, the main source of infection can be identified. Moscow represents such a source in Russia due to serving as the largest transport hub in the country.</p></sec><sec><title>Methods</title><p>Methods. Mathematical modeling and data analysis methods are used.</p></sec><sec><title>Results</title><p>Results. A predicted time lag between peaks of daily infections in Russia and Moscow is confirmed. Identified invariant parameters for COVID-19 epidemic waves can be used to predict the spread of the disease. The checks were carried out for the wave sequence for which predictions were made about the development of infection for Russia and when the recession following peak would occur. These forecasts for all waves were confirmed from the third to the last sixth waves to confirm the found pattern, which can be important for predicting future events.</p></sec><sec><title>Conclusions</title><p>Conclusions. The confirmed forecasts for the timing and rate of the recession can be used to make good predictions about the fifth and sixth waves of infection of the Omicron variant of the COVID-19 virus. Earlier predictions were confirmed by the statistical data.</p></sec></trans-abstract><kwd-group xml:lang="ru"><kwd>кинетическое уравнение</kwd><kwd>COVID-19</kwd><kwd>распространение волн</kwd></kwd-group><kwd-group xml:lang="en"><kwd>kinetic equation</kwd><kwd>COVID-19</kwd><kwd>wave propagation</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Acioli P.H. Diffusion as a first model of spread of viral infection. Am. J. 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Dokl. Phys. 2021;66(5):129–133. https://doi.org/10.1134/S1028335821050013 ] [Original Russian Text: Aristov V.V., Stroganov A.V., Yastrebov A. D. Application of a Kinetic Model for Studying the Spatial Spread of COVID-19. Doklady Rossiiskoi Akademii Nauk. Fizika, Tekhnicheskie nauki. 2021;498(1):27–32 (in Russ.). https://doi.org/10.31857/S2686740021030020 ]</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
