# On the intrinsic dimensionality of Covid-19 data: a global perspective

arXiv: https://arxiv.org/abs/2203.04165 · HTML: https://mlsanity.com/paper/2203.04165/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 49 references that are in the map.
- First posted on arXiv 8 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [The Intrinsic Dimension of Images and Its Impact on Learning](https://mlsanity.com/paper/2104.08894/) (2021)
- [Estimating the intrinsic dimension of datasets by a minimal neighborhood information](https://mlsanity.com/paper/1803.06992/) (2018)
- [Model-based clustering based on sparse finite Gaussian mixtures](https://mlsanity.com/paper/1606.06828/) (2016)
- [Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphs](https://mlsanity.com/paper/2005.03082/) (2020)
- [intRinsic: an R Package for Model-Based Estimation of the Intrinsic Dimension of a Dataset](https://mlsanity.com/paper/2102.11425/) (2021)
- [Identifying highly influential travellers for spreading disease on a public transport system](https://mlsanity.com/paper/2004.01581/) (2020)

## Built on by (most cited first, 1 of 1)

- [Density Estimation via Binless Multidimensional Integration](https://mlsanity.com/paper/2407.08094/) (2024)

## Abstract (arXiv)

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.