Multi-city modeling of epidemics using a topology-based SIR model: Neural network-enhanced SAIRD model

IF 3.7 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science Pub Date : 2025-10-04 DOI:10.1016/j.jocs.2025.102721

Achraf Zinihi , Moulay Rchid Sidi Ammi , Ahmed Bachir

引用次数: 0

Abstract

This paper presents a computationally efficient hybrid approach for multi-city epidemic modeling, utilizing a topology-based SIR model for individual cities coupled via empirical transportation networks to account for migration between them. Within each city, the epidemiological dynamics are described using an SAIRD model. This study introduces two key innovations: the self-consistent determination of coupling parameters to maintain the populations of individual cities, and the incorporation of distance-dependent temporal delays in migration. Our model is applied to China’s 3 populated cities. The results demonstrate the model’s effectiveness in capturing the complex dynamics of epidemic spread across multiple urban centers.

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基于拓扑的SIR模型的多城市流行病建模：神经网络增强的SAIRD模型

本文提出了一种计算效率高的多城市流行病建模混合方法，利用基于拓扑的SIR模型，通过经验交通网络耦合单个城市，以解释它们之间的迁移。在每个城市内，使用SAIRD模型描述流行病学动态。本研究引入了两个关键创新：自洽确定耦合参数以维持单个城市的人口，以及在迁移中纳入距离相关的时间延迟。我们的模型应用于中国三个人口密集的城市。结果表明，该模型在捕捉流行病在多个城市中心传播的复杂动态方面是有效的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

5.50

自引率

3.00%

发文量

227

审稿时长

41 days

期刊介绍： Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).