School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China.
School of Geospatial Information, University of Information Engineering, Zhengzhou 450001, China.
Math Biosci Eng. 2023 Jun 5;20(7):13086-13112. doi: 10.3934/mbe.2023583.
Outbreaks of infectious diseases pose significant threats to human life, and countries around the world need to implement more precise prevention and control measures to contain the spread of viruses. In this study, we propose a spatial-temporal diffusion model of infectious diseases under a discrete grid, based on the time series prediction of infectious diseases, to model the diffusion process of viruses in population. This model uses the estimated outbreak origin as the center of transmission, employing a tree-like structure of daily human travel to generalize the process of viral spread within the population. By incorporating diverse data, it simulates the congregation of people, thus quantifying the flow weights between grids for population movement. The model is validated with some Chinese cities with COVID-19 outbreaks, and the results show that the outbreak point estimation method could better estimate the virus transmission center of the epidemic. The estimated location of the outbreak point in Xi'an was only 0.965 km different from the actual one, and the results were more satisfactory. The spatiotemporal diffusion model for infectious diseases simulates daily newly infected areas, which effectively cover the actual patient infection zones on the same day. During the mid-stage of viral transmission, the coverage rate can increase to over 90%, compared to related research, this method has improved simulation accuracy by approximately 18%. This study can provide technical support for epidemic prevention and control, and assist decision-makers in developing more scientific and efficient epidemic prevention and control policies.
传染病的爆发对人类生命构成了重大威胁，世界各国需要采取更精确的预防和控制措施来遏制病毒的传播。在这项研究中，我们提出了一种基于传染病时间序列预测的离散网格下的传染病时空扩散模型，用于模拟病毒在人群中的扩散过程。该模型以估计的疫情爆发原点为传播中心，采用每日人类出行的树状结构来概括人群中病毒传播的过程。通过整合多种数据，该模型模拟了人群的聚集，从而量化了人口迁移中网格之间的流动权重。该模型通过一些发生过 COVID-19 疫情的中国城市进行了验证，结果表明，疫情爆发点估计方法可以更好地估计疫情的病毒传播中心。西安疫情爆发点的估计位置与实际位置仅相差 0.965 公里，结果更为理想。传染病的时空扩散模型模拟了每日新感染区域，有效地覆盖了当天实际患者的感染区域。在病毒传播的中期，覆盖率可以增加到 90%以上，与相关研究相比，该方法的模拟精度提高了约 18%。本研究可为疫情防控提供技术支持，协助决策者制定更科学、更有效的疫情防控政策。
