Darwish A., Leonenko V.N. Reducing computational costs of agent-based modeling of respiratory infection spread using a machine learning-based surrogate model. Научно-технический вестник информационных технологий, механики и оптики [Scientific and Technical Journal of Information Technologies, Mechanics and Optics]. 2025. Vol. 25. No. ?. pp. в печати?.
Korzin A., Leonenko V. Lightweight Models for Influenza and COVID-19 Prediction in Heterogeneous Populations: A Trade-Off Between Performance and Level of Detail. Mathematics. 2025. Vol. 13. No. 9. pp. 1385.. doi: 10.3390/math13091385
Урбанистические факторы динамики эпидемических ОРВИ: анализ на основе мультиагентых моделей
Korzin A.I., Leonenko V.N. Uncertainty Quantification for the Stochastic Modeling of Influenza Propagation: How Many Simulation Runs is Enough. 2024 IEEE 25th International Conference of Young Professionals in Electron Devices and Materials (EDM). 2024. pp. 2200-2203.. doi: 10.1109/EDM61683.2024.10615093
Leonenko V.N. When a Pandemic Comes to Town: Combating Propagation of Novel Viruses in Russian Cities Using Statistical and Mathematical Modeling. 3rd International Conference on Problems of Informatics, Electronics and Radio Engineering, PIERE 2024. 2024. pp. 990-995.. doi: 10.1109/PIERE62470.2024.10804935
Senichev S.D., Fandeev A.A., Leonenko V.N. Accelerating multiagent epidemic modeling with surrogate-based methods. 2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON). 2024. pp. 220-223.. doi: 10.1109/SIBIRCON63777.2024.10758463
Abramova Y., Leonenko V. The Past Helps the Future: Coupling Differential Equations with Machine Learning Methods to Model Epidemic Outbreaks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2024. Vol. 14835. pp. 247–254.. doi: 10.1007/978-3-031-63772-8_23
Тычкова В.И., Леоненко В.Н., Даниленко Д.М. Предсказательное моделирование эволюции респираторных вирусов: современные возможности и ограничения [Predictive Modeling of Respiratory Virus Evolution: Current Capabilities and Limitations]. Математическая биология и биоинформатика [Mathematical Biology and Bioinformatics]. 2024. Т. 19. № 2. С. 579-592.. doi: 10.17537/2024.19.579
Korzin A.I., Kaparulin T.I., Leonenko V.N. Assessing the Effect of Influenza Vaccination Strategies Using Multi-agent Modeling. 3rd International Conference on Problems of Informatics, Electronics and Radio Engineering, PIERE 2024. 2024. pp. 1000-1003.. doi: 10.1109/PIERE62470.2024.10805002
Korzin A.I., Kaparulin T.I., Leonenko V.N. Assessing the Applicability of the Multiagent Modeling Approach to the Epidemic Surveillance of COVID-19 in Russian Cities. 2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON). 2024. pp. 237-242.. doi: 10.1109/SIBIRCON63777.2024.10758442
Использование интерпретируемых алгоритмов машинного обучения для моделирования динамики заболеваемости во время эпидемии
Леоненко В.Н., Корзин А.И., Даниленко Д.М. Применение математических моделей динамики заболеваемости эпидемическими ОРВИ для увеличения эффективности эпидемиологического надзора [Application of Mathematical Models of the Dynamics of the Epidemic Acute Respiratory Viral Infections to Increase the Efficiency of Epidemiological Surveillance]. Математическая биология и биоинформатика [Mathematical Biology and Bioinformatics]. 2023. Т. 18. № 2. С. 517–542.. doi: 10.17537/2023.18.517
Sahatova K., Kharlunin A., Leonenko V. A Novel Approach to Modeling and Visualisation of Epidemic Outbreaks: Combining Manual and Automatic Calibration. Proceedings - 2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2023. 2023. pp. 221-226.. doi: 10.1109/CSGB60362.2023.10329850
прогнозирование распространения заболеваний на основе пространственно-временных данных с использованием сверточных нейронных сетей
Kharlunin A., Huaman I., Leonenko V. Inferring Values of Epidemic Indicators via SEIR Models to Enhance Epidemiological Surveillance in Russia. Proceedings - 2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2023. 2023. pp. 202-207.. doi: 10.1109/CSGB60362.2023.10329625
Huaman I., Leonenko V. Does Complex Mean Accurate: Comparing COVID-19 Propagation Models with Different Structural Complexity. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2023. Vol. 10475. pp. 270–277.. doi: 10.1007/978-3-031-36024-4_21
Sahatova K., Kharlunin A., Huaman I., Leonenko V. Accounting for Data Uncertainty in Modeling Acute Respiratory Infections: Influenza in Saint Petersburg as a Case Study. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2023. Vol. 10475. pp. 286-299.. doi: 10.1007/978-3-031-36024-4_23
Huaman I., Plesovskaya E.P., Leonenko V.N. Matching model complexity with data detail: influenza propagation modeling as a case study. 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON). 2022. pp. 650-654.. doi: 10.1109/SIBIRCON56155.2022.10017084
Leonenko V.N., Kaliberda Y., Muravyova Y.V., Artyukh V. A Decision Support Framework for Periprosthetic Joint Infection Treatment: A Cost-Effectiveness Analysis Using Two Modeling Approaches. Journal of Personalized Medicine. 2022. Vol. 12. No. 8. pp. 1216.. doi: 10.3390/jpm12081216
Matveeva A., Leonenko V. Application of Gaussian process regression as a surrogate modeling method to assess the dynamics of COVID-19 propagation. Procedia Computer Science. 2022. Vol. 212. pp. 340-347.. doi: 10.1016/j.procs.2022.11.018
Leonenko V. A Hybrid Modeling Framework for City-Scale Dynamics of Multi-strain Influenza Epidemics. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2022. Vol. 13352. pp. 164-177.. doi: 10.1007/978-3-031-08757-8_16
Pasala K., Putnikov S., Leonenko V.N. Calibrating deterministic compartmental models of infection dynamics using neural network and data sampling approaches. Proceedings - 2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2022. 2022. pp. 100-103.. doi: 10.1109/CSGB56354.2022.9865428
Вероятностные методы анализа данных: учебно-методическое пособие по выполнению лабораторных работ
Leonenko V.N. Herd immunity levels and multi-strain influenza epidemics in Russia: a modelling study. Russian Journal of Numerical Analysis and Mathematical Modelling. 2021. Vol. 36. No. 5. pp. 279-291.. doi: 10.1515/rnam-2021-0023
Danilenko D.M., Eropkin M.Y., Leonenko V.N., Konovalova N., Petrova P., Zheltukhina A., Vassilieva A. Assessment of rat polyclonal antisera's suitability in hemagglutination inhibition assay for influenza surveillance and antigenic mapping. Journal of Virological Methods. 2021. Vol. 293. pp. 114170.. doi: 10.1016/j.jviromet.2021.114170
Leonenko V.N. Modeling Co-circulation of Influenza Strains in Heterogeneous Urban Populations: The Role of Herd Immunity and Uncertainty Factors. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2021. Vol. 12744. pp. 663-669.. doi: 10.1007/978-3-030-77967-2_55
Leonenko V.N., Kaliberda Y., Artyuk V. A modeling framework for decision support in periprosthetic joint infection treatment. Studies in health technology and informatics. 2021. Vol. 285. pp. 106-111.. doi: 10.3233/SHTI210581
Kaliberda Y., Leonenko V.N., Artyukh V. Towards cost-effective treatment of periprosthetic joint infection: from statistical analysis to Markov models. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2021. Vol. 12744. pp. 494-505.. doi: 10.1007/978-3-030-77967-2_41
Leonenko V.N., Kovalchuk S.V. Analyzing the spatial distribution of individuals predisposed to arterial hypertension in Saint Petersburg using synthetic populations. ITM Web of Conferences. 2020. Vol. 31. pp. 03002.. doi: 10.1051/itmconf/20203103002
Leonenko V.N., Arzamastsev S., Bobashev G. Contact patterns and influenza outbreaks in Russian cities: A proof-of-concept study via agent-based modeling. Journal of Computational Science. 2020. Vol. 44. pp. 101156.. doi: 10.1016/j.jocs.2020.101156
Leonenko V.N., Danilenko D.M. Modeling the dynamics of population immunity to influenza in Russian cities. ITM Web of Conferences. 2020. Vol. 31. pp. 03001.. doi: 10.1051/itmconf/20203103001
Arzamastsev S.A., Leonenko V.N. A demographic microsimulation model for the long-term evolution of synthetic populations in Saint-Petersburg. Доклады Международной конференции "Математическая биология и биоинформатика". 2020. Vol. 8. pp. 157-161.. doi: 10.17537/icmbb20.29
Leonenko V.N. Analyzing the Spatial Distribution of Acute Coronary Syndrome Cases Using Synthesized Data on Arterial Hypertension Prevalence. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2020. Vol. 12140 LNCS. pp. 483-494.. doi: 10.1007/978-3-030-50423-6_36
Попова Е.П., Леоненко В.Н. Прогнозирование реакции пользователей в социальных сетях методами машинного обучения [Machine learning methods for forecasting of social network users’ reactio]. Научно-технический вестник информационных технологий, механики и оптики [Scientific and Technical Journal of Information Technologies, Mechanics and Optics]. 2020. Т. 20. № 1(125). С. 118-124.. doi: 10.17586/2226-1494-2020-20-1-118-124
Leonenko V.N., Lobachev A.I., Bobashev G. Spatial modeling of influenza outbreaks in Saint Petersburg using synthetic population. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2019. Vol. 11536. pp. 492-505.. doi: 10.1007/978-3-030-22734-0_36
Bates S., Leonenko V.N., Rineer J., Bobashev G. Using synthetic populations to understand geospatial patterns in opioid related overdose and predicted opioid misuse. Computational and Mathematical Organization Theory. 2019. Vol. 25. No. 1. pp. 36-47.. doi: 10.1007/s10588-018-09281-2
Leonenko V.N., Bobashev G. Analyzing influenza outbreaks in Russia using an age-structured dynamic transmission model. Epidemics. 2019. Vol. 29. pp. 100358.. doi: 10.1016/j.epidem.2019.100358
Карпова Л.С., Соминина А.А., Даниленко Д.М., Волик К.М., Леоненко В.Н. Оценка эффективности базовых линий и порогов интенсивности эпидемий по результатам традиционного надзора за гриппом [Evaluation of the effectiveness of baselines and thresholds intensity epidemics, according to the results of traditional surveillance for influenza]. Эпидемиология и вакцинопрофилактика [Epidemiologiya i Vaktsinoprofilaktika]. 2019. Т. 18. № 4. С. 4-13.. doi: 10.31631/2073-3046-2019-18-4-4-13
Owusu P.A., Leonenko V.N., Mamchik N.A., Skorb E.V. Modeling the growth of dendritic electroless silver colonies using hexagonal cellular automata. Procedia Computer Science. 2019. Vol. 156. pp. 43-48.. doi: 10.1016/j.procs.2019.08.128
Математическая эпидемиология: учебно-методическое пособие по выполнению лабораторных работ
Leonenko V.N., Novoselova Y.K. Influence of External Factors on Inter-City Influenza Spread in Russia: A Modeling Approach. Trends in Biomathematics: Modeling, Optimization and Computational Problems. 2018. pp. 375-389.. doi: 10.1007/978-3-319-91092-5_26
Bates S., Leonenko V.N., Rineer J., Bobashev G. Using synthetic populations to understand geospatial patterns in opioid related overdose and predicted opioid misuse. International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation, BRiMS 2018. 2018.
Leonenko V.N., Ivanov S.V. Prediction of influenza peaks in Russian cities: comparing the accuracy of two SEIR models. Mathematical Biosciences and Engineering. 2018. Vol. 15. No. 1. pp. 209–232.. doi: 10.3934/mbe.2018009
Leonenko V.N., Bochenina K.O., Kesarev S.A. Influenza peaks forecasting in Russia: assessing the applicability of statistical methods. Procedia Computer Science. 2017. Vol. 108. pp. 2363-2367.. doi: 10.1016/j.procs.2017.05.196
Seleznev N.E., Leonenko V.N. Absolute humidity anomalies and the influenza onsets in Russia: A computational study. Procedia Computer Science. 2017. Vol. 119. pp. 224-233.. doi: 10.1016/j.procs.2017.11.180
Анализ и моделирование темпоральных комплексных сетей
Seleznev N.E., Leonenko V.N. Boosting Performance of Influenza Outbreak Prediction Framework. Communications in Computer and Information Science. 2017. Vol. 745. pp. 374-384.. doi: 10.1007/978-3-319-69784-0_32
Анализ и моделирование темпоральных комплексных сетей
Artzrouni M., Leonenko V.N., Mara T.A. A syringe-sharing model for the spread of HIV: application to Omsk, Western Siberia. Mathematical Medicine and Biology. 2017. Vol. 34. No. 1. pp. 15-37.. doi: 10.1093/imammb/dqv036
Слоот П., Холыст Я., Кампис Ж., Лис М., Митягин С.А., Иванов С.В., Боченина К.О., Павлова В.Ю., Мухина К.Д., Насонов Д.А., Бутаков Н.А., Леоненко В.Н., Ланцева А.А., Бухановский А.В. Суперкомпьютерное моделирование критических явлений в сложных социальных системах. Научно-технический вестник информационных технологий, механики и оптики [Scientific and Technical Journal of Information Technologies, Mechanics and Optics]. 2016. Т. 16. № 6(106). С. 967-995.. doi: 10.17586/2226-1494-2016-16-6-967-995
Leonenko V.N., Ivanov S.V., Novoselova Y.K. A computational approach to investigate patterns of acute respiratory illness dynamics in the regions with distinct seasonal climate transitions. Procedia Computer Science. 2016. Vol. 80. pp. 2402-2413.. doi: 10.1016/j.procs.2016.05.538
Leonenko V.N., Novoselova Y.K., Ong K.M. Influenza Outbreaks Forecasting in Russian Cities: Is Baroyan-rvachev Approach Still Applicable?. Procedia Computer Science. 2016. Vol. 101. pp. 282-291.. doi: 10.1016/j.procs.2016.11.033
Леоненко В.Н., Новоселова Ю.К., Онг К. Предсказание пиков эпидемий гриппа в Санкт-Петербурге с помощью популяционных математических моделей. Научно-технический вестник информационных технологий, механики и оптики [Scientific and Technical Journal of Information Technologies, Mechanics and Optics]. 2016. Т. 16. № 6(106). С. 1145–1148.. doi: 10.17586/2226-1494-2016-16-6-1145-1148
Leonenko V.N., Ivanov S.V. Fitting the SEIR model of seasonal influenza outbreak to the incidence data for Russian cities. Russian Journal of Numerical Analysis and Mathematical Modelling. 2016. Vol. 31. No. 5. pp. 267-279.. doi: 10.1515/rnam-2016-0026
Leonenko V.N., Pertsev N.V., Artzrouni M. Using high performance algorithms for the hybrid simulation of disease dynamics on CPU and GPU. Procedia Computer Science. 2015. Vol. 51. pp. 150-159.. doi: 10.1016/j.procs.2015.05.214
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