  This paper aims to develop a global perspective of the complexity of the
relationship between the standardised per-capita growth rate of Covid-19 cases,
deaths, and the OxCGRT Covid-19 Stringency Index, a measure describing a
country's stringency of lockdown policies. To achieve our goal, we use a
heterogeneous intrinsic dimension estimator implemented as a Bayesian mixture
model, called Hidalgo. We identify that the Covid-19 dataset may project onto
two low-dimensional manifolds without significant information loss. The low
dimensionality suggests strong dependency among the standardised growth rates
of cases and deaths per capita and the OxCGRT Covid-19 Stringency Index for a
country over 2020-2021. Given the low dimensional structure, it may be feasible
to model observable Covid-19 dynamics with few parameters. Importantly, we
identify spatial autocorrelation in the intrinsic dimension distribution
worldwide. Moreover, we highlight that high-income countries are more likely to
lie on low-dimensional manifolds, likely arising from aging populations,
comorbidities, and increased per capita mortality burden from Covid-19.
Finally, we temporally stratify the dataset to examine the intrinsic dimension
at a more granular level throughout the Covid-19 pandemic.
