在相空间中分析流行病
Analysing pandemics in phase-space
AI总结：
本研究提出含时滞的改进SIRD模型，以相空间（易感人群为变量）分析新冠疫情，可在感染峰值后不久以约10%准确率预测德国前三波疫情总死亡数，并证明经典恒参SEIRD/SIRD模型无法准确描述首波疫情。
摘要：
基于SIRD模型（易感-感染-恢复-死亡模型），本研究提出了一种包含时间延迟的新模型，用于描述新型冠状病毒Sars-CoV-2大流行的暴发。所有数据均通过将所有物理量表示为易感人群的函数进行分析，而非通常的时间依赖形式。在德国新冠大流行的第一波、第二波和第三波疫情中，在感染人数达到峰值后不久，该模型就能以约10%的准确率预测总死亡人数。通过相空间表示法可以证明，参数恒定的经典SEIRD模型和SIRD模型无法准确描述第一波大流行的情况。
英文摘要：
Based on the SIRD-model a new model including time-delay is proposed for a description of the outbreak of the novel coronavirus Sars-CoV-2 pandemic. All data were analysed by representing all quantities as a function of the susceptible population, as opposed to the usual dependence on time. The total number of deaths could be predicted for the first, second and third wave of the pandemic in Germany with an accuracy of about 10\%, shortly after the maximum of infectious people was reached. By using the presentation in phase space, it could be shown that a classical SEIRD- and SIRD-model with constant parameters will not be able to describe the first wave of the pandemic accurately.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
