Diallo Diaoulé, Schönfeld Jurij, Blanken Tessa F, Hecking Tobias
Institute of Software Technology, German Aerospace Center (DLR), 51147 Cologne, Germany.
Department of Psychological Methods, University of Amsterdam, 1018WS Amsterdam, The Netherlands.
Entropy (Basel). 2024 Aug 19;26(8):703. doi: 10.3390/e26080703.
This study advances the field of infectious disease forecasting by introducing a novel approach to micro-level contact modeling, leveraging human movement patterns to generate realistic temporal-dynamic networks. Through the incorporation of human mobility models and parameter tuning, this research presents an innovative method for simulating micro-level encounters that closely mirror infection dynamics within confined spaces. Central to our methodology is the application of Bayesian optimization for parameter selection, which refines our models to emulate both the properties of real-world infection curves and the characteristics of network properties. Typically, large-scale epidemiological simulations overlook the specifics of human mobility within confined spaces or rely on overly simplistic models. By focusing on the distinct aspects of infection propagation within specific locations, our approach strengthens the realism of such pandemic simulations. The resulting models shed light on the role of spatial encounters in disease spread and improve the capability to forecast and respond to infectious disease outbreaks. This work not only contributes to the scientific understanding of micro-level transmission patterns but also offers a new perspective on temporal network generation for epidemiological modeling.
本研究通过引入一种全新的微观层面接触建模方法，利用人类移动模式生成逼真的时间动态网络，推动了传染病预测领域的发展。通过整合人类移动模型和参数调整，本研究提出了一种创新方法，用于模拟微观层面的接触，这些接触紧密反映了封闭空间内的感染动态。我们方法的核心是应用贝叶斯优化进行参数选择，这使我们的模型能够更好地模拟现实世界感染曲线的特性以及网络属性的特征。通常，大规模的流行病学模拟忽略了封闭空间内人类移动的细节，或者依赖过于简单的模型。通过关注特定地点内感染传播的不同方面，我们的方法增强了此类大流行模拟的现实性。由此产生的模型揭示了空间接触在疾病传播中的作用，并提高了预测和应对传染病爆发的能力。这项工作不仅有助于科学理解微观层面的传播模式，还为流行病学建模的时间网络生成提供了新的视角。
