Evidence map›Paper›PMID 33678199›Full record

ArticleEpidemiology and infection2021

The spatio-temporal distribution of COVID-19 infection in England between January and June 2020.

Richard Elson, Tilman M Davies, Iain R Lake, Roberto Vivancos, Paula B Blomquist, Andre Charlett, Gavin Dabrera

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Epidemiology and infection, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Observational
  3. Article
  4. Article
  5. Article
  6. Structural Inequality and COVID-19 Mortality in Chicago: An Ecological Analysis.Journal of racial and ethnic health disparities · 2023
    Article
  7. Article
  8. Article
  9. Exploring the Spatial Relative Risk of COVID-19 in Berlin-Neukölln.International journal of environmental research and public health · 2023
    Article
  10. Article
  11. Dynamic Demand Evaluation of COVID-19 Medical Facilities in Wuhan Based on Public Sentiment.International journal of environmental research and public health · 2022
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Exploring the effects of PMEnvironmental research · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Richard ElsonNational Infection Service, Public Health England, London, UK.ORCID 0000-0001-6350-5274
Tilman M DaviesDepartment of Mathematics & Statistics, University of Otago, Dunedin, New Zealand.
Iain R LakeSchool of Environmental Sciences, University of East Anglia, Norwich, UK.
Roberto VivancosNational Infection Service, Public Health England, London, UK.
Paula B BlomquistNational Infection Service, Public Health England, London, UK.
Andre CharlettNational Infection Service, Public Health England, London, UK.
Gavin DabreraNational Infection Service, Public Health England, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The spatio-temporal dynamics of an outbreak provide important insights to help direct public health resources intended to control transmission. They also provide a focus for detailed epidemiological studies and allow the timing and impact of interventions to be assessed.A common approach is to aggregate case data to administrative regions. Whilst providing a good visual impression of change over space, this method masks spatial variation and assumes that disease risk is constant across space. Risk factors for COVID-19 (e.g. population density, deprivation and ethnicity) vary from place to place across England so it follows that risk will also vary spatially. Kernel density estimation compares the spatial distribution of cases relative to the underlying population, unfettered by arbitrary geographical boundaries, to produce a continuous estimate of spatially varying risk.Using test results from healthcare settings in England (Pillar 1 of the UK Government testing strategy) and freely available methods and software, we estimated the spatial and spatio-temporal risk of COVID-19 infection across England for the first 6 months of 2020. Widespread transmission was underway when partial lockdown measures were introduced on 23 March 2020 and the greatest risk erred towards large urban areas. The rapid growth phase of the outbreak coincided with multiple introductions to England from the European mainland. The spatio-temporal risk was highly labile throughout.In terms of controlling transmission, the most important practical application of our results is the accurate identification of areas within regions that may require tailored intervention strategies. We recommend that this approach is absorbed into routine surveillance outputs in England. Further risk characterisation using widespread community testing (Pillar 2) data is needed as is the increased use of predictive spatial models at fine spatial scales.

Indexed as

Time FactorsCOVID-19EnglandHumansPopulation SurveillanceRisk Evaluation and MitigationRisk FactorsSpatio-Temporal AnalysisUrban PopulationCOVID-19health resourceskernel density estimationsevere acute respiratory syndrome coronavirus 2spatial analysis

Identifiers

PMID33678199
PMCPMC7985908

What OpenQuestion holds

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Registered trials

None linked

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.