ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2022
Unequal impact and spatial aggregation distort COVID-19 growth rates.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Localized transmission of an aquatic pathogen drives hidden epidemics and population collapse in a terrestrial host.Nature ecology & evolution · 2026Article
- The spatiotemporal dynamics of COVID-19 in Europe: time-series clustering maps 5 distinct trajectories to spatial patterns.Population health metrics · 2025Article
- Spatial scales of COVID-19 transmission in Mexico.PNAS nexus · 2024Article
- Equitable access to COVID-19 vaccines makes a life-saving difference to all countries.Nature human behaviour · 2022Article
- Data science approaches to infectious disease surveillance.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Review
- A comprehensive spatial-temporal infection model.Chemical engineering science · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic has posed unprecedented challenges to public health world-wide. To make decisions about mitigation strategies and to understand the disease dynamics, policy makers and epidemiologists must know how the disease is spreading in their communities. Here we analyse confirmed infections and deaths over multiple geographic scales to show that COVID-19's impact is highly unequal: many regions have nearly zero infections, while others are hot spots. We attribute the effect to a Reed-Hughes-like mechanism in which the disease arrives to regions at different times and grows exponentially at different rates. Faster growing regions correspond to hot spots that dominate spatially aggregated statistics, thereby skewing growth rates at larger spatial scales. Finally, we use these analyses to show that, across multiple spatial scales, the growth rate of COVID-19 has slowed down with each surge. These results demonstrate a trade-off when estimating growth rates: while spatial aggregation lowers noise, it can increase bias. Public policy and epidemic modelling should be aware of, and aim to address, this distortion. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.