Evidence map›Paper›PMID 35942006›Full record

ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2022

Nowcasting COVID-19 deaths in England by age and region.

Shaun R Seaman, Pantelis Samartsidis, Meaghan Kall, Daniela De Angelis

Abstract read
In one paragraph

Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Nowcasting the 2022 mpox outbreak in England.PLoS computational biology · 2023
    Article
  5. Article
  6. Article
  7. Nowcasting COVID-19 deaths in England by age and region.Journal of the Royal Statistical Society. Series C, Applied statistics · 2022
    Article
  8. Bayesian imputation of COVID-19 positive test counts for nowcasting under reporting lag.Journal of the Royal Statistical Society. Series C, Applied statistics · 2022
    Article
  9. Article
  10. 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

4 authors.

Shaun R SeamanMRC Biostatistics Unit University of Cambridge Cambridge Cambridgeshire UK.
Pantelis SamartsidisMRC Biostatistics Unit University of Cambridge Cambridge Cambridgeshire UK.
Meaghan KallCOVID-19 National Epidemiology Cell UK Health Security Agency London UK.
Daniela De AngelisMRC Biostatistics Unit University of Cambridge Cambridge Cambridgeshire UK.

Funding

Medical Research Council MC_UU_00002/10Medical Research Council MC_UU_00002/11
6 · The paper itself

Abstract

Understanding the trajectory of the daily number of COVID-19 deaths is essential to decisions on how to respond to the pandemic, but estimating this trajectory is complicated by the delay between deaths occurring and being reported. In England the delay is typically several days, but it can be weeks. This causes considerable uncertainty about how many deaths occurred in recent days. Here we estimate the deaths per day in five age strata within seven English regions, using a Bayesian model that accounts for reporting-day effects and longer-term changes in the delay distribution. We show how the model can be computationally efficiently fitted when the delay distribution is the same in multiple strata, for example, over a wide range of ages.

Indexed as

epidemic monitoringgeneralised Dirichletreporting delayright‐truncation

Identifiers

PMID35942006
PMCPMC9349735

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.