  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.
