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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="brief-report" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Russian Journal of Infection and Immunity</journal-id><journal-title-group><journal-title xml:lang="en">Russian Journal of Infection and Immunity</journal-title><trans-title-group xml:lang="ru"><trans-title>Инфекция и иммунитет</trans-title></trans-title-group></journal-title-group><issn publication-format="print">2220-7619</issn><issn publication-format="electronic">2313-7398</issn><publisher><publisher-name xml:lang="en">SPb RAACI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">17693</article-id><article-id pub-id-type="doi">10.15789/2220-7619-TSA-17693</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>SHORT COMMUNICATIONS</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>КРАТКИЕ СООБЩЕНИЯ</subject></subj-group><subj-group subj-group-type="article-type"><subject>Short Communication</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Time series analysis for modeling and predicting confirmed cases of influenza a in Algeria</article-title><trans-title-group xml:lang="ru"><trans-title>Анализ временных рядов для моделирования и прогнозирования подтвержденных случаев гриппа а в Алжире</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Seba</surname><given-names>Djillali</given-names></name><name xml:lang="ru"><surname>Себа</surname><given-names>Джиллали</given-names></name></name-alternatives><address><country country="DZ">Algeria</country></address><bio xml:lang="en"><p>д.мат.н., доцент, лаборатория прикладной математики, факультет математики</p></bio><bio xml:lang="ru"><p>Doctor in Mathematics, Assistant Professor, Laboratory of Applied Mathematics, Department of Mathematics</p></bio><email>d.seba@esi-sba.dz</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Benaklef</surname><given-names>N.</given-names></name><name xml:lang="ru"><surname>Бенаклеф</surname><given-names>Н.</given-names></name></name-alternatives><address><country country="DZ">Algeria</country></address><bio xml:lang="en"><p>PhD Student in Mathematics, Speciality “Probability and Statistics”, Member of Applied Mathematics Laboratory</p></bio><bio xml:lang="ru"><p>аспирант по математике, специальность «Вероятность и статистика», лаборатория прикладной математики в Университете </p></bio><email>d.seba@esi-sba.dz</email><xref ref-type="aff" rid="aff2"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Belaide</surname><given-names>K.</given-names></name><name xml:lang="ru"><surname>Белаиде</surname><given-names>К.</given-names></name></name-alternatives><address><country country="DZ">Algeria</country></address><bio xml:lang="en"><p>Doctor in Mathematics, Full Professor, Laboratory of Applied Mathematics, Department of Mathematics</p></bio><bio xml:lang="ru"><p>д.мат.н., профессор, лаборатория прикладной математики, факультет математики</p></bio><email>d.seba@esi-sba.dz</email><xref ref-type="aff" rid="aff2"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Higher School of Informatics</institution></aff><aff><institution xml:lang="ru">Высшая школа информатики</institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">University of Bejaia</institution></aff><aff><institution xml:lang="ru">Университет Беджаи</institution></aff></aff-alternatives><pub-date date-type="preprint" iso-8601-date="2024-07-30" publication-format="electronic"><day>30</day><month>07</month><year>2024</year></pub-date><pub-date date-type="pub" iso-8601-date="2025-04-30" publication-format="electronic"><day>30</day><month>04</month><year>2025</year></pub-date><volume>15</volume><issue>1</issue><issue-title xml:lang="en"/><issue-title xml:lang="ru"/><fpage>168</fpage><lpage>172</lpage><history><date date-type="received" iso-8601-date="2024-06-14"><day>14</day><month>06</month><year>2024</year></date><date date-type="accepted" iso-8601-date="2024-07-29"><day>29</day><month>07</month><year>2024</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2025, Seba D., Benaklef N., Belaide K.</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2025, Себа Д., Бенаклеф Н., Белаиде К.</copyright-statement><copyright-year>2025</copyright-year><copyright-holder xml:lang="en">Seba D., Benaklef N., Belaide K.</copyright-holder><copyright-holder xml:lang="ru">Себа Д., Бенаклеф Н., Белаиде К.</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/4.0</ali:license_ref></license></permissions><self-uri xlink:href="https://iimmun.ru/iimm/article/view/17693">https://iimmun.ru/iimm/article/view/17693</self-uri><abstract xml:lang="en"><p>Influenza A is a subtype of the influenza virus that primarily infects birds and mammals, causing respiratory illness. It is characterized by its ability to mutate rapidly, leading to various strains and occasional pandemics. Objective. This paper is dedicated to studying the distribution behavior and predicting confirmed cases of Influenza A within the Algerian context, a highly infectious dis- ease that causes widespread illness and deaths both in Algeria and globally. <italic>Materials and methods.</italic> To predict confirmed cases of Influenza A, we implemented several statistical models, including ARIMA, Seasonal ARIMA (SARIMA), ETS, BATS, and the machine learning technique RNN, which is widely recognized in the literature. We then conducted a comparative study using performance measures to evaluate these models. <italic>Results.</italic> We used RMSE to determine the best-performing model. Our findings indicate that RNN outperformed the others due to its ability to handle complex patterns, including seasonal components and memory. SARIMA and BATS also performed well, thanks to their capacity to manage seasonal patterns. In contrast, ARIMA and ETS showed the poorest performance. <italic>Conclusion.</italic> This study employed a comprehensive approach to develop a model for predicting confirmed cases of Influenza A in Algeria. The results enhance our understanding of the potential future behavior of this disease and contribute to effective risk management strategies.</p></abstract><trans-abstract xml:lang="ru"><p>Грипп А является подтипом вируса гриппа, который в первую очередь поражает птиц и млекопитающих, вызывая респираторные заболевания, и характеризуется способностью быстро мутировать, что приводит к появлению разнообразия штаммов и периодическим пандемиям. Настоящая статья посвящена изучению в Алжире распространения и прогнозированию подтвержденных случаев гриппа А, высокоинфекционного заболевания, которое вызывает широко распространенные заболевания и смертность как в Алжире, так и во всем мире. <italic>Материалы и методы.</italic> Для прогнозирования подтвержденных случаев гриппа А были применены несколько статистических моделей, включая ARIMA, Seasonal ARIMA (SARIMA), ETS, BATS и широко признанный метод машинного обучения RNN. Далее мы провели сравнительное исследование с использованием показателей производительности для оценки указанных моделей. <italic>Результаты.</italic> Для определения наиболее эффективной модели проводилась оценка среднеквадратической ошибки. Наши результаты показывают, что RNN превзошел другие модели благодаря своей способности обрабатывать сложные шаблоны, включая сезонные компоненты и наличию памяти. SARIMA и BATS также показали хорошие результаты благодаря своей способности управлять сезонными закономерностями. Напротив, ARIMA и ETS показали самые плохие результаты. <italic>Вывод.</italic> В приводимом исследовании использовался комплексный подход для разработки модели прогнозирования подтвержденных случаев гриппа A в Алжире. Полученные результаты расширяют наше понимание потенциального будущего распространения данного заболевания и способствуют эффективным стратегиям управления рисками.</p></trans-abstract><kwd-group xml:lang="en"><kwd>influeza A</kwd><kwd>prediction</kwd><kwd>risk management</kwd><kwd>time series</kwd><kwd>BATS</kwd><kwd>Algeria</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>грипп A</kwd><kwd>прогнозирование</kwd><kwd>управление рисками</kwd><kwd>временные ряды</kwd><kwd>BATS</kwd><kwd>Алжир</kwd></kwd-group><funding-group/></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>Ali S.T., Cowling B.J. 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