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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">37516</article-id><article-id pub-id-type="doi">10.22363/2658-4670-2023-31-4-359-374</article-id><article-id pub-id-type="edn">FZWSUR</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">Demographic indicators, models, and testing</article-title><trans-title-group xml:lang="ru"><trans-title>Демографические показатели, модели и проверка</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-5017-8331</contrib-id><name-alternatives><name xml:lang="en"><surname>Shilovsky</surname><given-names>Gregory A.</given-names></name><name xml:lang="ru"><surname>Шиловский</surname><given-names>Г. А.</given-names></name></name-alternatives><bio xml:lang="en"><p>Candidate of Biological Sciences, Senior Researcher in Laboratory 6 at Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute); Researcher in Faculty of Biology at Lomonosov Moscow State University</p></bio><email>gregory_sh@list.ru</email><xref ref-type="aff" rid="aff1"/><xref ref-type="aff" rid="aff2"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-4746-6396</contrib-id><name-alternatives><name xml:lang="en"><surname>Seliverstov</surname><given-names>Alexandr V.</given-names></name><name xml:lang="ru"><surname>Селиверстов</surname><given-names>А. В.</given-names></name></name-alternatives><bio xml:lang="en"><p>Candidate of Physical and Mathematical Sciences, Leading Researcher in Laboratory 6</p></bio><email>slvstv@iitp.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-8546-364X</contrib-id><name-alternatives><name xml:lang="en"><surname>Zverkov</surname><given-names>Oleg A.</given-names></name><name xml:lang="ru"><surname>Зверков</surname><given-names>О. А.</given-names></name></name-alternatives><bio xml:lang="en"><p>Candidate of Physical and Mathematical Sciences, Researcher in Laboratory 6</p></bio><email>zverkov@iitp.ru</email><xref ref-type="aff" rid="aff1"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute)</institution></aff><aff><institution xml:lang="ru">Институт проблем передачи информации имени А. А. Харкевича РАН</institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">Lomonosov Moscow State University</institution></aff><aff><institution xml:lang="ru">Московский государственный университет имени М. В. Ломоносова</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2023-12-15" publication-format="electronic"><day>15</day><month>12</month><year>2023</year></pub-date><volume>31</volume><issue>4</issue><issue-title xml:lang="en">VOL 31, NO4 (2023)</issue-title><issue-title xml:lang="ru">ТОМ 31, №4 (2023)</issue-title><fpage>359</fpage><lpage>374</lpage><history><date date-type="received" iso-8601-date="2024-01-19"><day>19</day><month>01</month><year>2024</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2023, Shilovsky G.A., Seliverstov A.V., Zverkov O.A.</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2023, Шиловский Г.А., Селиверстов А.В., Зверков О.А.</copyright-statement><copyright-year>2023</copyright-year><copyright-holder xml:lang="en">Shilovsky G.A., Seliverstov A.V., Zverkov O.A.</copyright-holder><copyright-holder xml:lang="ru">Шиловский Г.А., Селиверстов А.В., Зверков О.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/37516">https://journals.rudn.ru/miph/article/view/37516</self-uri><abstract xml:lang="en"><p style="text-align: justify;">The use of simple demographic indicators to describe mortality dynamics can obscure important features of the survival curve, particularly during periods of rapid change, such as those caused by internal or external factors, and especially at the oldest or youngest ages. Therefore, instead of the generally accepted Gompertz method, other methods based on demographic indicators are often used. In human populations, chronic phenoptosis, in contrast to age-independent acute phenoptosis, is characterized by rectangularization of the survival curve and an accompanying increase in average life expectancy at birth, which can be attributed to advances in society and technology. Despite the simple geometric interpretation of the phenomenon of rectangularization of the survival curve, it is difficult to notice one, detecting changes in the optimal coefficients in the Gompertz-Makeham law due to high computational complexity and increased calculation errors. This is avoided by calculating demographic indicators such as the Keyfitz entropy, the Gini coefficient, and the coefficient of variation in lifespan. Our analysis of both theoretical models and real demographic data shows that with the same value of the Gini coefficient in the compared cohorts, a larger value of the Keyfitz entropy indicates a greater proportion of centenarians relative to average life expectancy. On the contrary, at the same value of the Keyfitz entropy, a larger value of the Gini coefficient corresponds to a relatively large mortality at a young age. We hypothesize that decreases in the Keyfitz entropy may be attributable to declines in background mortality, reflected in the Makeham term, or to reductions in mortality at lower ages, corresponding to modifications in another coefficient of the Gompertz law. By incorporating dynamic shifts in age into survival analyses, we can deepen our comprehension of mortality patterns and aging mechanisms, ultimately contributing to the development of more reliable methods for evaluating the efficacy of anti-aging and geroprotective interventions used in gerontology.</p></abstract><trans-abstract xml:lang="ru"><p style="text-align: justify;">Используя простые демографические показатели для описания динамики смертности, можно скрыть важные особенности кривой выживания, особенно в периоды быстрых изменений, вызванных, например, внутренними или внешними факторами, и особенно в самом старшем или самом молодом возрасте. Поэтому вместо общепринятого метода Гомпертца часто используются другие методы, основанные на демографических показателях. У человека хронический феноптоз, в отличие от возрастно-независимого острого феноптоза, проявляется ректангуляризацией кривой выживания с одновременным увеличением средней продолжительности жизни при рождении в результате развития общества и научно-технического прогресса. Несмотря на простую геометрическую интерпретацию явления ректангуляризации кривой выживания, его трудно заметить, прослеживая лишь изменения оптимальных коэффициентов в законе Гомпертца-Мейкхама из-за высокой вычислительной сложности, а также увеличения погрешности расчёта. Этого можно избежать путём расчёта демографических показателей, таких как энтропия Кейфитца, коэффициент Джини и коэффициент вариации продолжительности жизни. Как теоретические примеры, так и расчёты, основанные на реальных демографических данных, показывают, что при одинаковом значении коэффициента Джини в сравниваемых когортах большее значение энтропии Кейфитца указывает на большую долю долгожителей относительно средней продолжительности жизни. Напротив, при том же значении энтропии Кейфитца большее значение коэффициента Джини соответствует относительно большой смертности в молодом возрасте. Мы предполагаем, что уменьшение энтропии Кейфица может быть связано со снижением фоновой смертности, отражённой в модели Мейкхама, или со снижением смертности в более раннем возрасте, что соответствует изменениям в другом коэффициенте закона Гомпертца. Другой причиной может быть снижение смертности в малых возрастах, что соответствует уменьшению другого коэффициента в законе Гомпертца. Включив динамические возрастные изменения в анализ выживаемости, мы можем углубить наше понимание моделей смертности и механизмов старения, что в конечном итоге внесёт вклад в разработку более надёжных методов оценки эффективности мер против старения и геропротекторов, используемых в геронтологии.</p></trans-abstract><kwd-group xml:lang="en"><kwd>lifespan</kwd><kwd>demographic indicator</kwd><kwd>Keyfitz entropy</kwd><kwd>Gini coefficient</kwd><kwd>coefficient of variation</kwd><kwd>phenoptosis</kwd><kwd>aging</kwd><kwd>Gompertz law</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>продолжительность жизни</kwd><kwd>демографический показатель</kwd><kwd>энтропия Кейфитца</kwd><kwd>коэффициент Джини</kwd><kwd>коэффициент вариации</kwd><kwd>феноптоз</kwd><kwd>старение</kwd><kwd>закон Гомпертца</kwd></kwd-group><funding-group><funding-statement xml:lang="en">Computations were performed at the Joint Supercomputer Center of the Russian Academy of Sciences (JSCC RAS).</funding-statement></funding-group></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>D. 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