论文标题
通过通用共卷19岁的强大控制措施了解感染的进展
Understanding infection progression under strong control measures through universal COVID-19 growth signatures
论文作者
论文摘要
据报道，Covid-19确认的病例计数的广泛生长特征在三个不同的动力学方案（指数，超线性和sublinear）之间进行了急剧过渡。通过分析和数值分析，开发了一个新的框架，以利用这些签名中的信息。采用了一种众所周知的方法，在该方法中，人们寻找常见的动力学特征，而与其他因素的差异无关。这些特征和相关的缩放定律被用作有效的区域有效区域的强大工具，了解疾病进展的定性变化并推断关键感染参数。经验观察到的Covid-19增长模式的联合分析和数值分析的开发框架可能会导致对强大控制措施下感染进展的基本了解，这适用于Covid-19和其他感染性疾病的爆发。
Widespread growth signatures in COVID-19 confirmed case counts are reported, with sharp transitions between three distinct dynamical regimes (exponential, superlinear and sublinear). Through analytical and numerical analysis, a novel framework is developed that exploits information in these signatures. An approach well known to physics is applied, where one looks for common dynamical features, independently from differences in other factors. These features and associated scaling laws are used as a powerful tool to pinpoint regions where analytical derivations are effective, get an insight into qualitative changes of the disease progression, and infer the key infection parameters. The developed framework for joint analytical and numerical analysis of empirically observed COVID-19 growth patterns can lead to a fundamental understanding of infection progression under strong control measures, applicable to outbursts of both COVID-19 and other infectious diseases.
