ilvmНормативно-правовое регулирование в ветеринарииLegal regulation in veterinary medicine2782-6252SpbGUVM Publishing House10.52419/issn2782-6252.2025.4.59ilvm-956Research ArticleРЕЗУЛЬТАТЫ НАУЧНЫХ ИССЛЕДОВАНИЙ В ВЕТЕРИНАРИИ. ИНФЕКЦИОННЫЕ БОЛЕЗНИRESULTS OF SCIENTIFIC RESEARCH IN VETERINARY MEDICINE: INFECTIOUS DISEASESРазличные подходы к синтетическому моделированию эпизоотического процесса инфекционных болезней животных для эффективного принятия управленческих решенийVarious approaches to synthetic modeling of the epizootic process of infectious animal diseases for effective management decision-makinghttps://orcid.org/0000-0002-3658-5886ПлемяшовК. В.PlemyashovK. V.

Кирилл Владимирович Племяшов - д-р ветеринар. наук, профессор, член-корр. РАН, ректор 

Kirill Vl. Plemyashov - Dr. of Veterinary Sciences, Professor, Corresponding Member of the Russian Academy of Sciences, Rector

https://orcid.org/0000-0002-6689-3468КузьминВ. А.KuzminV. A.

Владимир Александрович Кузьмин - д-р ветеринар. наук, профессор, кафедра эпизоотологии 

Vladimir Al. Kuzmin - Dr. of Veterinary Sciences, Professor, Department of Epizootology

kuzmin@epizoo.ruhttps://orcid.org/0000-0002-7858-1947ОреховД. А.OrekhovD. A.

Дмитрий Андреевич Орехов - канд. вет. наук, доцент, заведующий, кафедра организации, экономики, управления ветеринарным делом 

Dmitry An. Orekhov - Candidate of Veterinary Sciences, Associate Professor, Head of the Department of Organization, Economics, and Management of Veterinary Medicine

https://orcid.org/0000-0003-1158-7460ЩербаковП. П.ShcherbakovP. P.

Павел Петрович Щербаков - канд. физ.-мат. наук, доцент, кафедра информационных систем в искусстве и гуманитарных науках 

Pavel P. Shcherbakov - Candidate of Physics and Mathematics, Associate Professor, Department of Information Systems in Arts and Humanities

https://orcid.org/0000-0002-1671-5524БорисовН. В.BorisovN. V.

Николай Валентинович Борисов - д-р физ.-мат. наук, профессор, заведующий, кафедра информационных систем в искусстве и гуманитарных науках 

Nikolay V. Borisov - Dr. of Physics and Mathematics, Professor, Head of the Department of Information Systems in Arts and Humanities

КротовЛ. Н.KrotovL. N.

Леонид Николаевич Кротов - канд. ветеринар. наук, начальник 

Leonid N. Krotov - Candidate of Veterinary Sciences, Head of the Department of Veterinary Medicine

Санкт-Петербургский государственный университет ветеринарной медициныРоссияSaint Petersburg State University of Veterinary MedicineRussian FederationСанкт-Петербургский государственный университетРоссияSaint Petersburg State UniversityRussian FederationУправление ветеринарии Ленинградской областиРоссияLeningrad Region Veterinary AdministrationRussian Federation202511022026045965Copyright © Племяшов К.В., Кузьмин В.А., Орехов Д.А., Щербаков П.П., Борисов Н.В., Кротов Л.Н., 20252025Племяшов К.В., Кузьмин В.А., Орехов Д.А., Щербаков П.П., Борисов Н.В., Кротов Л.Н.Plemyashov K.V., Kuzmin V.A., Orekhov D.A., Shcherbakov P.P., Borisov N.V., Krotov L.N.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ilvm.elpub.ru/jour/article/view/956

В обзорной статье представлены результаты конкретной задачи по пониманию движущих факторов эпизоотии АЧС (в сравнении с другими опасными инфекциями) и созданию искусственной модели эпизоотического процесса (ЭП) данной инфекции в проекте PPApred на двух изолированных островах во Франции. Пятью независимыми международными группами исследователей из разных стран Европы были проведены: 1) сравнение подходов к моделированию; 2) оценка способности их прогнозов по пространственно-временному распространению эпизоотии на территориях ферм с домашними свиньями и граничащих с ними лесных угодий с дикими кабанами; 3) определение приоритетности ограниченного числа альтернативных вмешательств в ЭП. Наиболее эффективные модели для прогнозирования эпизоотии АЧС различались в зависимости от фазы заражения животных, вида хозяина и предвидения пространственной или временной динамики ЭП. Пяти командам, успешно завершившим этот конкурс в двух этапах прогнозирования (по одному месяцу каждый), было предложено разработать окончательную модель, соответствующую синтетическим данным на базе трёх различных этапов эпизоотии АЧС (возникновение, развитие, затухание); спрогнозировать пространственно-временное развитие эпизоотии; оценить эффективность ограниченного числа стратегий управления ЭП из предложенных пяти вариантов. Совместная итоговая модель, построенная с использованием прогнозов от всех пяти международных групп, превзошла каждую из индивидуальных моделей как минимум в одном этапе эпизоотии АЧС. Моделирование такой эмерджентной и опасной инфекции, как АЧС, на примере синтетических ветеринарно-значимых данных продемонстрировало значение учёта всех факторов взаимодействия между домашними свиньями и дикими кабанами в ЭП, как основы повышения эффективности борьбы с будущими эпизоотиями АЧС и готовности ветеринарных специалистов к новым эмерджентным инфекциям.

This review article presents the results of a specific task to understand the driving factors of ASF epizootics (in comparison with other dangerous infections) and to create an artificial model of the epizootic process (EP) of this infection in the PPApred project on two isolated islands in France. Five independent international research groups from different European countries conducted: 1) a comparison of modelling approaches; 2) an assessment of the ability of their forecasts for the spatio-temporal spread of the epizootic in the territories of farms with domestic pigs and adjacent forest lands with wild boars; 3) prioritization of a limited number of alternative interventions in the epizootic environment. The most effective models for predicting ASF epizootics varied depending on the phase of animal infection, the host species, and the prediction of the spatial or temporal dynamics of the epizootic environment. The five teams that successfully completed this competition in two forecasting stages (one month each) were asked to develop a final model corresponding to synthetic data based on three different stages of the ASF epizootic (emergence, development, attenuation); predict the spatiotemporal development of the epizootic; and evaluate the effectiveness of a limited number of EP management strategies from the five proposed options. The combined final model, constructed using forecasts from all five international groups, outperformed each of the individual models in at least one stage of the ASF epizootic. Modeling an emerging and dangerous infection such as ASF, using synthetic veterinary-relevant data, demonstrated the importance of considering all factors of interaction between domestic pigs and wild boars in the EP as a basis for improving the effectiveness of combating future ASF epizootics and the preparedness of veterinary specialists for new emerging infections.

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The authors declare that there are no conflicts of interest present.