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2070-7428

Общество с ограниченной ответственностью "Издательский Дом "Академия Естествознания"

10.17513/spno.34689

ART-34689

МАТЕМАТИЧЕСКОЕ МОДЕЛИРОВАНИЕ В ЭПИДЕМИОЛОГИИ ЛЕПРЫ: МЕТОДОЛОГИЧЕСКИЕ ПОДХОДЫ И ОБОСНОВАНИЕ ВЫБОРА МОДЕЛИ

Янчевская

Е.Ю.

Yanchevskaya

E.Y.

apteca-111a@mail.ru

Сердюков

А.Г.

Serdyukov

A.G.

agma@astranet.ru

Воронина

Л.П.

Voronina

L.P.

voroninaluda74@mail.ru

Федеральное государственное бюджетное образовательное учреждение высшего образования «Астраханский государственный медицинский университет» Министерства здравоохранения Российской Федерации Astrakhan State Medical University

10 07 2026

7 15 15

This is an open-access article distributed under the terms of the CC BY 4.0 license.

https://science-education.ru/ru/article/view?id=34689

надёжного инструментария для прогнозирования и оценки вмешательств. Цель - систематически проанализировать методологические подходы к математическому моделированию в эпидемиологии лепры и обосновать алгоритм выбора оптимальной модели в зависимости от исследовательского вопроса и доступных данных. Проведён анализ литературы в базах данных PubMed/MEDLINE, Scopus и Web of Science за 1970–2024 гг.; в список литературы включено 32 источника. В мировой практике применяются три класса моделей: детерминированные компартментные (SIR, SEIR, SIMLEP, SIMCOLEP), стохастические индивидуально ориентированные и модели временны́х рядов (ARIMA/SARIMA). Компартментные и стохастические модели наиболее целесообразны для оценки R₀ и долгосрочного сценарного анализа; SARIMA показала применимость в отдельных исследованиях краткосрочного прогнозирования по данным надзора. Выявлены ключевые методологические проблемы: занижение регистрации случаев, параметрическая неопределённость, роль зоонозного резервуара и пространственная гетерогенность заболеваемости. Обоснованы минимальные структурные требования к адекватной модели лепры и предложен пятишаговый алгоритм выбора типа модели. Выбор модели должен определяться исследовательским вопросом, горизонтом прогноза и качеством данных; стандартизация подходов к отчётности о моделях является необходимым условием для трансляции результатов в политику здравоохранения.

Leprosy (Hansen's disease) remains a significant public health problem: approximately 200,000 new cases are registered worldwide annually, and the WHO elimination goals for 2030 require reliable tools for forecasting and intervention assessment. Aim - to systematically analyze methodological approaches to mathematical modeling in leprosy epidemiology and justify a decision-making algorithm for optimal model selection depending on the research question and available data. A literature search was conducted in PubMed/MEDLINE, Scopus, and Web of Science databases for the period 1970–2024; 32 sources were included in the reference list. Three classes of models are used in global practice: deterministic compartmental models (SIR, SEIR, SIMLEP, SIMCOLEP), stochastic individual-based simulations, and time series models (ARIMA/SARIMA). Compartmental and stochastic models are most appropriate for estimating R₀ and long-term scenario analysis; SARIMA has demonstrated applicability in selected short-term forecasting studies based on surveillance data. Key methodological challenges were identified: underreporting of cases, parametric uncertainty, the role of the zoonotic reservoir, and spatial heterogeneity of incidence. Minimum structural requirements for an adequate leprosy model are substantiated, and a five-step model selection algorithm is proposed. Model choice should be determined by the research question, forecast horizon, and data quality; standardization of modeling and reporting approaches is a necessary condition for translating results into public health policy.

лепра болезнь хансена математическое моделирование компартментные модели arima sarima анализ временны́х рядов базовое репродуктивное число эпидемиологический надзор элиминация

leprosy hansen's disease mathematical modeling compartmental models arima sarima time series analysis basic reproduction number epidemiological surveillance elimination

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