A novel spatio-temporal prediction model of epidemic spread integrating cellular automata with agent-based modeling

IF 5.3 1区 数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Chaos Solitons & Fractals Pub Date : 2024-11-04 DOI:10.1016/j.chaos.2024.115709
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Abstract

Since 2019, major infectious disease outbreaks have placed tremendous pressure on global public health systems, triggering extensive research on the predictive modeling of infectious diseases. Cellular Automaton (CA) is primarily used in the spatial prediction of infectious diseases to establish a model to for simulating the interaction between different regions and the infection risk to simulate the transmission process of the disease and predict its development trend. However, CA models are governed by initial fixed rules and local interactions, and often fail to capture the complex dynamics of epidemic transmission, which are influenced by factors such as public behavior and government intervention. In view of these limitations, we propose a factorial simulation model for the spatial spread of epidemics, the CA-ABM, which divides agents into three categories–public, government, and hospital agents–to comprehensively express the macro factors that affect the development of epidemics. Agent-Based Modeling (ABM) influences the transition rules of the CA through agent choices, constraints and supporting behaviors. Focusing on the COVID-19 pandemic in mainland China from February 6 to March 20, 2020, we simulate its spread. The results showed an average improvement of 8.4 % in prediction accuracy, with few errors, RMSE under 200, and R2 values over 0.9 in most provinces, demonstrating strong macro-scale stability. This approach helps regions to understand influencing factors and enables targeted infection risk assessment and prevention. In addition, scenario analysis based on CA-ABM model changes epidemic decision-making from “prediction-response” to “scenario-response” and provides theoretical reference for future epidemic management.
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将细胞自动机与基于代理的建模相结合的新型流行病传播时空预测模型
2019 年以来,重大传染病的爆发给全球公共卫生系统带来了巨大压力,引发了对传染病预测建模的广泛研究。细胞自动机(Cellular Automaton,CA)主要用于传染病的空间预测,建立模拟不同区域间相互作用和感染风险的模型,模拟疾病的传播过程,预测其发展趋势。然而,CA 模型受初始固定规则和局部相互作用的制约,往往无法捕捉到受公众行为和政府干预等因素影响的流行病传播的复杂动态。鉴于这些局限性,我们提出了一种疫情空间传播的因子模拟模型--CA-ABM,将代理分为公众代理、政府代理和医院代理三类,以全面表达影响疫情发展的宏观因素。基于代理的建模(ABM)通过代理的选择、约束和支持行为来影响 CA 的过渡规则。我们以 2020 年 2 月 6 日至 3 月 20 日在中国大陆发生的 COVID-19 大流行为重点,模拟了其传播情况。结果表明,预测精度平均提高了 8.4%,误差很小,RMSE 小于 200,大多数省份的 R2 值超过 0.9,显示了较强的宏观稳定性。这种方法有助于各地区了解影响因素,从而有针对性地进行感染风险评估和预防。此外,基于 CA-ABM 模型的情景分析将疫情决策从 "预测-响应 "转变为 "情景-响应",为未来的疫情管理提供了理论参考。
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来源期刊
Chaos Solitons & Fractals
Chaos Solitons & Fractals 物理-数学跨学科应用
CiteScore
13.20
自引率
10.30%
发文量
1087
审稿时长
9 months
期刊介绍: Chaos, Solitons & Fractals strives to establish itself as a premier journal in the interdisciplinary realm of Nonlinear Science, Non-equilibrium, and Complex Phenomena. It welcomes submissions covering a broad spectrum of topics within this field, including dynamics, non-equilibrium processes in physics, chemistry, and geophysics, complex matter and networks, mathematical models, computational biology, applications to quantum and mesoscopic phenomena, fluctuations and random processes, self-organization, and social phenomena.
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