AI 中文总结
本研究提出贝叶斯时变SEIARD模型，结合多源数据与聚类分析，揭示美国各州COVID-19流行的区域异质性，为医疗资源分配和公共卫生干预评估提供支持。
AI 中文摘要
我们使用改进的基于人群的易感-暴露-感染-无症状-康复-死亡（SEIARD）仓室模型，对美国各州的COVID-19传播动态进行回顾性分析。该框架引入了时变传播率、报告率和死亡率，以捕捉大流行期间公众行为和政策干预的时间变化。具体而言，传播率被建模为人口流动性的函数（源自谷歌流动性报告），并带有一个残差时间衰减项，用于捕捉行为适应和控制措施等未观测因素的净效应，而报告率则与全国检测策略相关联。我们采用贝叶斯方法整合多源数据并量化模型参数的不确定性。该模型明确区分了有症状和无症状感染者，并通过动态报告函数将潜在的流行状态与可观测量（包括报告病例和死亡数）关联起来。此回顾性建模框架为州级流行轨迹提供了见解，并支持数据驱动决策，以优化医疗资源分配及评估未来大流行期间的公共卫生干预措施。我们进一步对后验参数估计值应用聚类分析，以识别具有相似流行病学特征的美国州组，揭示了传播强度、繁殖动态和死亡负担方面存在显著的区域异质性。
英文摘要
We conduct a retrospective analysis of COVID-19 transmission dynamics across U.S. states using a modified population-based Susceptible-Exposed-Infectious-Asymptomatic-Recovered-Deceased (SEIARD) compartmental model. The proposed framework introduces time-varying transmission, reporting, and mortality rates to capture temporal variations in public behavior and policy interventions during the pandemic. In particular, the transmission rate is modeled as a function of population mobility (derived from Google Mobility Reports), with a residual time-decay term capturing the net effect of unobserved factors such as behavioral adaptation and control measures, while reporting is linked to nationwide testing strategies. We employ a Bayesian approach to integrate multiple data sources and quantify uncertainties in model parameters. The model explicitly distinguishes between symptomatic and asymptomatic infectious individuals and links the latent epidemic states to observable quantities, including reported cases and deaths, through a dynamic reporting function. This retrospective modeling framework provides insights into state-level epidemic trajectories and supports data-driven decision-making for optimal allocation of healthcare resources and evaluation of public health interventions during future pandemics. We further apply a clustering analysis to the posterior parameter estimates to identify groups of U.S. states exhibiting similar epidemiological characteristics, revealing substantial regional heterogeneity in transmission intensity, reproduction dynamics, and mortality burden.
