Alves Caroline L, Kuhnert Katharina, Rodrigues Francisco Aparecido, Moeckel Michael
Center for Scientific Services and Transfer, Aschaffenburg University of Applied Sciences, Aschaffenburg, Germany.
Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil.
Biol Methods Protoc. 2025 Jun 5;10(1):bpaf039. doi: 10.1093/biomethods/bpaf039. eCollection 2025.
The coronavirus disease 2019 (COVID-19) pandemic has necessitated the development of accurate models to predict disease dynamics and guide public health interventions. This study leverages the COVASIM agent-based model to simulate 1331 scenarios of COVID-19 transmission across various social settings, focusing on the school, community, and work contact layers. We extracted complex network measures from these simulations and applied deep learning algorithms to predict key epidemiological outcomes, such as infected, severe, and critical cases. Our approach achieved an value exceeding 95%, demonstrating the model's robust predictive capability. Additionally, we identified optimal intervention strategies using spline interpolation, revealing the critical roles of community and workplace interventions in minimizing the pandemic's impact. The findings underscore the value of integrating network analytics with deep learning to streamline epidemic modeling, reduce computational costs, and enhance public health decision-making. This research offers a novel framework for effectively managing infectious disease outbreaks through targeted, data-driven interventions.
2019年冠状病毒病（COVID-19）大流行使得开发准确的模型以预测疾病动态并指导公共卫生干预措施成为必要。本研究利用基于代理的COVASIM模型来模拟1331种COVID-19在各种社会环境中的传播情况，重点关注学校、社区和工作接触层面。我们从这些模拟中提取复杂网络指标，并应用深度学习算法来预测关键的流行病学结果，如感染、重症和危重症病例。我们的方法实现了超过95%的 值，证明了该模型强大的预测能力。此外，我们使用样条插值法确定了最佳干预策略，揭示了社区和工作场所干预措施在最小化大流行影响方面的关键作用。研究结果强调了将网络分析与深度学习相结合以简化疫情建模、降低计算成本并加强公共卫生决策的价值。本研究提供了一个通过有针对性的、数据驱动的干预措施有效管理传染病暴发的新颖框架。
