Индивидуумориентированное моделирование эпидемических ОРВИ в городах РФ: методы реализации и оценки применимости

Индивидуум-ориентированное моделирование эпидемических ОРВИ в городах РФ: методы реализации и оценки применимости

Корзин А. И., Чичкова Н. А., Капарулин Т. И., Леоненко В. Н.
Сибирский журнал индустриальной математики, 28, 4(104), 108-129 (2025)
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УДК 519.876.5 
DOI: 10.33048/SIBJIM.2025.28.408

Аннотация:

Индивидуум-ориентированные модели эпидемических вспышек завоевали широкую популярность среди исследователей общественного здравоохранения благодаря их способности описывать эпидемический процесс с высокой детализацией. Основным недостатком таких моделей является необходимость подготовки большого количества детализированных данных в качестве входа модели, а также выделения мощных вычислительных ресурсов для выполнения расчётов. Как следствие, из-за недостаточности данных и больших временных затрат при идентификации таких моделей их объяснительная и предсказательная сила может быть поставлена под вопрос. В настоящем исследовании предлагается метод разработки индивидуум-ориентированных моделей эпидемических вспышек ОРВИ, которые могут быть удовлетворительно откалиброваны на имеющиеся данные демографической и эпидемической статистики в городах РФ и позволяют получить результаты моделирования за разумное время за счёт применения процедуры сэмплинга. Прозрачность модельной структуры, применение модульного принципа на уровне алгоритма (разделение демографической и эпидемической составляющих), а также открытость кода программы позволяет обеспечить принцип воспроизводимости результатов моделирования, независимую проверку результатов и потенциальное их переиспользование со стороны исследовательских коллективов, занимающихся смежными темами. Оценка применимости метода произведена на примере моделирования вспышек гриппа в Самаре и Челябинске.

Литература:
  1. Wang P., Zheng X., Liu H. Simulation and forecasting models of COVID-19 taking into account spatio-temporal dynamic characteristics // Review. Frontiers in Public Health. 2022, V. 10. Article number 1033432.
     
  2. Маслова И. И., Манолов А. И., Глущенко О. Е., Козлов И. E., Цуркис В. И., Попов Н. С., Самойлов А. Е., Лукашев А. Н., Ильина Е. Н. Ограничения в создании искусственных популяций в агентном моделировании эпидемий: систематический обзор // Журн. микробиологии, эпидемиологии и иммунобиологии. 2024. Т. 101, No 4. С. 4.
     
  3. Friedman J., Liu P., Troeger C. E. et al. Predictive performance of international COVID-19 mortality forecasting models // Nature Communications. 2021. V. 12, N 1. P. 2609.
     
  4. Ioannidis J. P., Cripps S., Tanner M. Forecasting for COVID-19 has failed // Intern. J. Forecasting. 2022. V. 38, N 2. P. 423–438.
     
  5. Stroud P., Del Valle S., Sydoriak S. et al. Spatial dynamics of pandemic influenza in a massive artificial society // J. Artificial Societies and Social Simulation. 2007. V. 10, N 4. P. 9.
     
  6. JASSS-Covid19-Thread // Review of Artificial Societies and Social Simulation; https://rofasss.org/tag/JASSS-Covid19-Thread/ 
     
  7. Lorig F., Johansson E., Davidsson P. Agent-based social simulation of the covid-19 pandemic: A systematic review // J. Artif. Soc. Soc. Simul. 2021. 24(3); DOI: 10.18564/jasss.4601
     
  8. Grefenstette J. J., Brown S. T., Rosenfeld R. et al. FRED (A Framework for Reconstructing Epidemic Dynamics): An open-source software system for modeling infectious diseases and control strategies using census-based populations // BMC Public Health. 2013. V. 13, N 1. P. 940.
     
  9. Cooley P., Brown S., Cajka J. et al. The role of subway travel in an influenza epidemic: a New York City simulation // J. Urban Health. 2011. V. 88. P. 982–995.
     
  10. Kerr C. C., Stuart R. M., Mistry D., Abeysuriya R. G., Rosenfeld K., Hart G. R., Nunez R. C., Cohen J. A., Selvaraj P., Hagedorn B., George L. Covasim: an agent-based model of COVID-19 dynamics and interventions // PLOS Computational Biology. 2021. V. 17, N 7. Article number e1009149.
     
  11. Hinch R., Probert W. J., Nurtay A., Kendall M., Wymant C., Hall M., Lythgoe K., Bulas Cruz A., Zhao L., Stewart A., Ferretti L. OpenABM-Covid19—An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing // PLoS Computational Biology. 2021. V. 17, N 7. Article number e1009146.
     
  12. Manout O., Ciari F. Assessing the role of daily activities and mobility in the spread of COVID-19 in Montreal with an agent-based approach // Frontiers in Built Environment. 2021. V. 7. Article number 654279.
     
  13. Mniszewski S. M., Del Valle S. Y., Stroud P. D., Riese J. M., Sydoriak S. J. EpiSimS simulation of a multicomponent strategy for pandemic influenza // Proceed. of the 2008 Spring Simulation Multiconf. 2008, April. 2008. P. 556–563.
     
  14. Tuomisto J. T., Yrjola J., Kolehmainen M., Bonsdorff J., Pekkanen J., Tikkanen T. An agent-based epidemic model REINA for COVID-19 to identify destructive policies // MedRxiv. P. 2020-04; DOI:10.1101/2020.04.09.20047498 
     
  15. Leonenko V., Lobachev A., Bobashev G. Spatial modeling of influenza outbreaks in Saint Petersburg using synthetic populations // InInternat. Conf. Comput. Sci., 2019 Jun 8. Cham: Springer International Publishing, 2019. P. 492–505.
     
  16. 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 // IEEE Internat. Multi-Conf. Engrg., Computer and Information Sci. (SIBIRCON), 2024, September. 2004. P. 237–242. IEEE.
     
  17. URL: https://github.com/vnleonenko/Multiagent_ARI
     
  18. Wheaton W. D., Cajka J. C., Chasteen B. M. et al. Synthesized population databases: A US geospatial database for agent-based models // Methods report (RTI Press). 2009. V. 2009, N 10. P. 905.
     
  19. Leonenko V., Arzamastsev S., Bobashev G. Contact patterns and influenza outbreaks in Russian cities: A proof-of-concept study via agent-based modeling // J. Comput. Sci. 2020. V. 44. Article number 101156.
     
  20.  Kontsevik G., Sokol A., Bogomolov Y. et al. Modeling the citizens’ settlement in residential buildings // Procedia Comput. Sci. 2022. V. 212. P. 51–63.
     
  21. Платформа «Цифровая урбанистика»; URL: https://dc.idu.actcognitive.org/
     
  22. Библиотека «population-restorator»; URL: https://github.com/kanootoko/population-restorator/
     
  23. Zakharov K., Aghajanyan A., Kovantsev A., Boukhanovsky A. Forecasting Population Migration in Small Settlements Using Generative Models under Conditions of Data Scarcity // Smart Cities. 2024. V. 7. P. 2495–2513; https://doi.org/10.3390/smartcities7050097
     
  24. Библиотека «ITMO-2» // URL: https://github.com/aghajanyan/ITMO-2/
     
  25. Геоинформационная система QGIS // URL: https://qgis.org/ru/site/
     
  26. Электронный справочник карт городов 2GIS // URL: https://2gis.ru
     
  27. Яндекс.Карты // https://yandex.ru/maps
     
  28. Яндекс.Аудитории // URL: https://audience.yandex.ru/
     
  29. Jennings V., Lloyd-Smith B., Ironmonger D. Household size and the Poisson distribution //J. Australian Population Association. 1999. V. 16. N 1. P. 65–84.
     
  30. Shamil M. S., Farheen F., Ibtehaz N. et al. An agent-based modeling of COVID-19: validation, analysis, and recommendations // Cognitive Computation. 2021. P. 1–12.
     
  31. Kagho G. O., Meli J., Walser D., Balac M. Effects of population sampling on agent-based transport simulation of on-demand services // Procedia Comput. Sci. 2022. V. 201. P. 305–312.
     
  32. Llorca C., Moeckel R. Effects of scaling down the population for agent-based traffic simulations // Procedia Comput. Sci. 2019. N 151. P. 782–787.
     
  33. Korzin A. I., Kaparulin T. I., Leonenko V. N. Assessing the Effect of Influenza Vaccination Strategies Using Multi-agent Modeling // In 2024 IEEE 3rd International Conference on Problems of Informatics, Electronics and Radio Engineering (PIERE), 2024, November. IEEE. 2024. P. 1000–1003.
     
  34. Leonenko V. N., Ivanov S. V. Fitting the SEIR model of seasonal influenza outbreak to the incidence data for Russian cities //Russian J. Numerical Analysis and Math. Modelling. 2016. V. 31. N 5. P. 267–279.
     
  35. Harris J. E. Critical role of the subways in the initial spread of SARS-CoV-2 in New York City // Frontiers in Public Health. 2021. V. 9. Article number 754767. 
     
  36. Leonenko V. A Hybrid Modeling Framework for City-Scale Dynamics of Multi-strain Influenza Epidemics // Proc. Internat. Conf. Comput. Sci. 2022. V. 13352. P. 164–177; DOI: 10.1007/978-3-031-08757-8_16
     
  37. Lee J. S., Filatova T., Ligmann-Zielinska A., Hassani-Mahmooei B., Stonedahl F., Lorscheid I., Voinov A., Polhill J. G., Sun Z., Parker D. C. The complexities of agent-based modeling output analysis // J. Artif. Soc. Soc. Simul. 2015. 18(4); DOI:10.18564/jasss.2897
     
  38. Castro B. M., Reis M. D. M., Salles R. M. Multi-agent simulation model updating and forecasting for the evaluation of COVID-19 transmission // Scientific Reports. 2022. V. 12, N 1. Article number 22091.

Исследование выполнено при финансовой поддержке Российского научного фонда (проект № 22-71-10067). Других источников финансирования проведения или руководства данным конкретным исследованием не было.

А. И. Корзин
  1. Университет ИТМО, 
    Кронверкский просп., 49, лит. А., г. Санкт-Петербург 197101, Россия

E-mail: corzin.an@gmail.com 

Н. А. Чичкова
  1. Университет ИТМО, 
    Кронверкский просп., 49, лит. А., г. Санкт-Петербург 197101, Россия

E-mail: nachichkova@itmo.ru 

Т. И. Капарулин
  1. Университет ИТМО, 
    Кронверкский просп., 49, лит. А., г. Санкт-Петербург 197101, Россия

E-mail: kaparulinti@mail.ru 

В. Н. Леоненко
  1. Университет ИТМО, 
    Кронверкский просп., 49, лит. А., г. Санкт-Петербург 197101, Россия
  2. НИИ гриппа им. Смородинцева Минздрава РФ, 
    ул. проф. Попова, 15/17, г. Санкт-Петербург 197022, Россия

E-mail: vnleonenko@itmo.ru 

Статья поступила 28.03.2024 г.
После доработки — 30.10.2025 г.
Принята к публикации 10.12.2025 г.

Abstract:

Individual-based models of epidemic outbreaks have gained wide popularity among public health researchers due to their ability to describe the epidemic process with high detail. The main disadvantage of such models is the need to prepare a large amount of detailed data as an input to the model, as well as to allocate powerful computing resources to perform calculations. As a consequence, due to the lack of data and difficulties in performing the identification procedure of such models, their explanatory and predictive power can be called into question. In this study, a method is proposed for developing individual-based models of ARVI epidemic outbreaks that can be satisfactorily calibrated on the available demographic and epidemic statistics data in the cities of the Russian Federation in a reasonable time, taking into account the actual availability of data and the degree of detail of the infection transmission patterns in a heterogeneous population at the level of individuals. By using the sampling procedure, an increase in the speed of the modeling program is achieved. The transparency of the model structure, the use of the modular principle at the algorithm level (separation of the demographic and epidemic components), and the openness of the program code allow us to ensure the principle of reproducibility of the modeling results, independent verification of the results, and their potential reuse by research teams working on related topics. The applicability of the methods is assessed using the example of modeling influenza outbreaks in Samara and Chelyabinsk.

References:
  1. Wang P., Zheng X., Liu H. Simulation and forecasting models of COVID-19 taking into account spatio-temporal dynamic characteristics. Review. Frontiers in Public Health, 2022, Vol. 10, Article number 1033432.
     
  2. Maslova I. I., Manolov A. I., Glushchenko O. E., Kozlov I. E., Tsurkis V. I., Popov N. S., Samoilov A. E., Lukashev A. N., Il’ina E. N. Ogranicheniya v sozdanii iskusstvennykh populyatsii v agentnom modelirovanii epidemii: sistematicheskii obzor [Limitations in the creation of artificial populations in agent-based epidemic modeling: a systematic review]. Zhurn. mikrobiologii, epidemiologii i immunobiologii [J. Microbiology, Epidemiology and Immunobiology], 2024, Vol. 101, No. 4, pp. 4.
     
  3. Friedman J., Liu P., Troeger C. E. et al. Predictive performance of international COVID-19 mortality forecasting models. Nature Communications, 2021, Vol. 12, No. 1, pp. 2609.
     
  4. Ioannidis J. P., Cripps S., Tanner M. Forecasting for COVID-19 has failed. Intern. J. Forecasting, 2022, Vol. 38, No. 2, pp. 423–438.
     
  5. Stroud P., Del Valle S., Sydoriak S. et al. Spatial dynamics of pandemic influenza in a massive artificial society. J. Artificial Societies and Social Simulation, 2007, Vol. 10, No. 4, pp. 9.
     
  6. JASSS-Covid19-Thread // Review of Artificial Societies and Social Simulation; https://rofasss.org/tag/JASSS-Covid19-Thread/
     
  7. Lorig F., Johansson E., Davidsson P. Agent-based social simulation of the covid-19 pandemic: A systematic review // J. Artif. Soc. Soc. Simul., 2021, Vol. 24, No. 3, Article number 5; DOI: 10.18564/jasss.4601
     
  8. Grefenstette J. J., Brown S. T., Rosenfeld R. et al. FRED (A Framework for Reconstructing Epidemic Dynamics): An open-source software system for modeling infectious diseases and control strategies using census-based populations. BMC Public Health, 2013, Vol. 13, No. 1, pp. 940.
     
  9. Cooley P., Brown S., Cajka J. et al. The role of subway travel in an influenza epidemic: a New York City simulation. J. Urban Health, 2011, Vol. 88, pp. 982–995.
     
  10. Kerr C. C., Stuart R. M., Mistry D., Abeysuriya R. G., Rosenfeld K., Hart G. R., Nunez R. C., Cohen J. A., Selvaraj P., Hagedorn B., George L. Covasim: an agent-based model of COVID-19 dynamics and interventions. PLOS Computational Biology, 2021, Vol. 17, No. 7, Article number e1009149.
     
  11. Hinch R., Probert W. J., Nurtay A., Kendall M., Wymant C., Hall M., Lythgoe K., Bulas Cruz A., Zhao L., Stewart A., Ferretti L. OpenABM-Covid19—An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing. PLoS Computational Biology, 2021, Vol. 17, No. 7, Article number e1009146.
     
  12. Manout O., Ciari F. Assessing the role of daily activities and mobility in the spread of COVID19 in Montreal with an agent-based approach. Frontiers in Built Environment 2021, Vol. 7, Article number 654279.
     
  13. Mniszewski S. M., Del Valle S. Y., Stroud P. D., Riese J. M.,Sydoriak S. J. EpiSimS simulation of a multicomponent strategy for pandemic influenza. Proceed. of the 2008 Spring Simulation Multiconf., 2008, April, pp. 556–563. 
     
  14. Tuomisto J. T., Yrjola J., Kolehmainen M., Bonsdorff J., Pekkanen J., Tikkanen T. An agentbased epidemic model REINA for COVID-19 to identify destructive policies. MedRxiv., pp.2020-04; DOI:10.1101/2020.04.09.20047498 
     
  15. Leonenko V., Lobachev A., Bobashev G. Spatial modeling of influenza outbreaks in Saint Petersburg using synthetic populations. InInternat. Conf. Comput. Sci., 2019 Jun 8, Cham: Springer International Publishing, 2019, pp. 492–505.
     
  16. 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. IEEE Internat. Multi-Conf. Engrg., Computer and Information Sci. (SIBIRCON), 2024, September, pp. 237–242.
     
  17. URL: https://github.com/vnleonenko/Multiagent_ARI
     
  18. Wheaton W. D., Cajka J. C., Chasteen B. M. et al. Synthesized population databases: A US geospatial database for agent-based models. Methods report, 2009, Vol. 2009, No. 10, pp. 905.
     
  19. Leonenko V., Arzamastsev S., Bobashev G. Contact patterns and influenza outbreaks in Russian cities: A proof-of-concept study via agent-based modeling. J. Comput. Sci., 2020, Vol. 44. Article number 101156.
     
  20. Kontsevik G., Sokol A., Bogomolov Y. et al. Modeling the citizens’ settlement in residential buildings. Procedia Comput. Sci. 2022, Vol. 212, pp. 51–63.
     
  21. Platforma «Tsifrovaya urbanistika» [The «Digital Urbanism» platform]; URL: https://dc.idu.actcognitive.org/
     
  22. Biblioteka «population-restorator» [Library «population-restorator»]; URL: https://github.com/kanootoko/population-restorator/ 
     
  23. Zakharov K., Aghajanyan A., Kovantsev A., Boukhanovsky A. Forecasting Population Migration in Small Settlements Using Generative Models under Conditions of Data Scarcity. Smart Cities, 2024, Vol. 7, pp. 2495–2513; https://doi.org/10.3390/smartcities7050097
     
  24. Biblioteka «ITMO-2» [Library «ITMO-2»]; URL: https://github.com/aghajanyan/ITMO-2/
     
  25. Geoinformatsionnaya sistema QGIS [Geographic information system QGIS]; URL: https://qgis.org/ru/site/
     
  26. Elektronnyi spravochnik kart gorodov 2GIS [Electronic directory of city maps 2GIS]; URL: https://2gis.ru
     
  27. Yandex.Karty [Yandex.Maps]; https://yandex.ru/maps
     
  28. Yandex.Auditorii [Yandex.Audiences]; URL: https://audience.yandex.ru/
     
  29. Jennings V., Lloyd-Smith B., Ironmonger D. Household size and the Poisson distribution. J. Australian Population Association, 1999, Vol. 16, No. 1, pp. 65–84.
     
  30. Shamil M. S., Farheen F., Ibtehaz N. et al. An agent-based modeling of COVID-19: validation, analysis, and recommendations. Cognitive Computation, 2021, pp. 1–12.
     
  31. Kagho G. O., Meli J., Walser D., Balac M. Effects of population sampling on agent-based transport simulation of on-demand services. Procedia Comput. Sci., 2022, Vol. 201, pp. 305–312.
     
  32. Llorca C., Moeckel R. Effects of scaling down the population for agent-based traffic simulations. Procedia Comput. Sci., 2019, No. 151, pp. 782–787.
     
  33. Korzin A. I., Kaparulin T. I., Leonenko V. N. Assessing the Effect of Influenza Vaccination Strategies Using Multi-agent Modeling. IEEE 3rd International Conference on Problems of Informatics, Electronics and Radio Engineering (PIERE), 2024, November, 2024, pp. 1000–1003.
     
  34. Leonenko V. N., Ivanov S. V. Fitting the SEIR model of seasonal influenza outbreak to the incidence data for Russian cities. Russian J. Numerical Analysis and Math. Modelling., 2016, Vol. 31, No. 5, pp. 267–279.
     
  35. Harris J. E. Critical role of the subways in the initial spread of SARS-CoV-2 in New York City. Frontiers in Public Health, 2021, Vol. 9, Article number 754767.
     
  36. Leonenko V. A Hybrid Modeling Framework for City-Scale Dynamics of Multi-strain Influenza Epidemics. Proc. Internat. Conf. Comput. Sci., 2022, Vol. 13352, pp. 164–177; DOI: 10.1007/978-3-031-08757-8_16
     
  37. Lee J. S., Filatova T., Ligmann-Zielinska A., Hassani-Mahmooei B., Stonedahl F., Lorscheid I., Voinov A., Polhill J. G., Sun Z., Parker D. C. The complexities of agent-based modeling output analysis. J. Artif. Soc. Soc. Simul., 2015, 18(4); DOI:10.18564/jasss.2897
     
  38. Castro B. M., Reis M. D. M., Salles R. M. Multi-agent simulation model updating and forecasting for the evaluation of COVID-19 transmission. Scientific Reports, 2022, Vol. 12, No. 1, Article number 22091.