Guo Jiaming, Yan Beibei, Yang Yanli, Ning Yifei, Zhou Yan, Zhang Rui, Ma Yifei, Li Jiantao, Yu Hongmei, Xie Jun
School of Public Health, Shanxi Medical University, Taiyuan, China.
Third Hospital of Shanxi Medical University, Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China.
Front Public Health. 2025 Jul 2;13:1582024. doi: 10.3389/fpubh.2025.1582024. eCollection 2025.
This study aims to assess the impact of inter-regional population mobility on epidemic progression and healthcare resource congestion during acute infectious disease outbreaks, providing a scientific basis for population control and healthcare resource allocation during pandemics.
Using the SARS-CoV-2 pandemic as a case study, we selected two city pairs-"Taiyuan-Jinzhong" and "Linfen-Yuncheng" as research subjects. Based on real SARS-CoV-2 transmission data and the Baidu Migration Index, we constructed a dynamic healthcare resource model incorporating population mobility factors. We quantified epidemic transmission and healthcare resource congestion using three indicators: cumulative cases, the onset time of healthcare resource congestion, and its duration. By analyzing these metrics, we explored the effect of migration rates on infection scale and healthcare resource congestion.
The model-fitted curves closely aligned with the actual data, with most observed data points falling within the 95% confidence interval. Our results suggest that population mobility affects both cumulative cases and healthcare resource congestion, with variation between regions. Unidirectional restrictions on population movement reduced cumulative cases in the outflow region, delayed the onset of healthcare resource congestion and shortened its duration; however, they also increased cumulative cases in the inflow region, advanced the onset of healthcare resource congestion and prolonged its duration. Bidirectional movement restrictions increased cumulative cases in high-prevalence regions, but did not change the onset time of healthcare resource congestion and either maintained or increased its duration. In contrast, in low-prevalence regions, bidirectional restrictions reduced cumulative cases, maintained the onset time of healthcare resource congestion, and either shortened or maintained its duration.
The healthcare resource dynamics model provides an effective framework for simulating the interplay between population mobility, epidemic transmission, and the congestion on medical resources. In the event of an infectious disease outbreak, this model can be integrated into a regionally unified platform for healthcare resource allocation. By incorporating real-time epidemic data and the healthcare capacities of different areas, the model enables targeted interventions based on the principles of zoning, classification, and time-based management. This approach helps to contain the spread of the epidemic, ease pressure on medical systems, and minimize socio-economic disruptions.
本研究旨在评估急性传染病暴发期间区域间人口流动对疫情进展和医疗资源拥堵的影响，为大流行期间的人口管控和医疗资源分配提供科学依据。
以新冠疫情为例，选取“太原—晋中”和“临汾—运城”两对城市作为研究对象。基于真实的新冠病毒传播数据和百度迁徙指数，构建了纳入人口流动因素的动态医疗资源模型。我们使用三个指标量化疫情传播和医疗资源拥堵情况：累计病例数、医疗资源拥堵的起始时间及其持续时间。通过分析这些指标，探讨了迁移率对感染规模和医疗资源拥堵的影响。
模型拟合曲线与实际数据紧密吻合，大多数观测数据点落在95%置信区间内。我们的结果表明，人口流动会影响累计病例数和医疗资源拥堵情况，且不同地区存在差异。对人口流动的单向限制减少了流出地区的累计病例数，推迟了医疗资源拥堵的起始时间并缩短了其持续时间；然而，这也增加了流入地区的累计病例数，提前了医疗资源拥堵的起始时间并延长了其持续时间。双向流动限制增加了高流行地区的累计病例数，但没有改变医疗资源拥堵的起始时间，且维持或增加了其持续时间。相比之下，在低流行地区，双向限制减少了累计病例数，维持了医疗资源拥堵的起始时间，并缩短或维持了其持续时间。
医疗资源动态模型为模拟人口流动、疫情传播和医疗资源拥堵之间的相互作用提供了一个有效的框架。在传染病暴发时，该模型可整合到区域统一的医疗资源分配平台中。通过纳入实时疫情数据和不同地区的医疗能力，该模型能够基于分区、分类和时间管理原则进行有针对性的干预。这种方法有助于遏制疫情传播，缓解医疗系统压力，并将社会经济干扰降至最低。
