Modeling healthcare resource dynamics and its application based on interregional population mobility

医疗保健 人口 资源(消歧) 大流行 持续时间(音乐) 医学 地理 计算机科学 环境卫生 疾病 2019年冠状病毒病(COVID-19) 传染病(医学专业) 经济 经济增长 病理 艺术 文学类 计算机网络
作者
Jiaming Guo,Beibei Yan,Yanli Yang,Yifei Ning,Yan Zhou,Rui Zhang,Yifei Ma,Jiantao Li,Hongmei Yu,Jun Xie
出处
期刊:Frontiers in Public Health [Frontiers Media]
卷期号:13
标识
DOI:10.3389/fpubh.2025.1582024
摘要

Objective 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. Methods 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. Results 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. Conclusion 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.
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