Evidence map›Paper›PMID 35013705›Full record

ArticleSpatial statistics2022

Application of Bayesian spatial-temporal models for estimating unrecognized COVID-19 deaths in the United States.

Yuzi Zhang, Howard H Chang, A Danielle Iuliano, Carrie Reed

Open access · greenAbstract read
In one paragraph

Article in Spatial statistics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.3field-weighted citation impact, top 12% of its field
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

12 citing papers in PubMed, 18 citations in OpenAlex.

  1. Article
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  4. A Bayesian spatial-temporal varying coefficients model for estimating excess deaths associated with respiratory infections.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2025
    Article
  5. Article
  6. Excess natural-cause mortality in US counties and its association with reported COVID-19 deaths.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
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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 at 2 institutions in 1 country.

Yuzi ZhangDepartment of Biostatistics and Bioinformatics, The Rollins School of Public Health of Emory University, 1518 Clifton Rd. N.E., Atlanta, GA 30322, USA.
Howard H ChangDepartment of Biostatistics and Bioinformatics, The Rollins School of Public Health of Emory University, 1518 Clifton Rd. N.E., Atlanta, GA 30322, USA.
A Danielle IulianoInfluenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Carrie ReedInfluenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Centers for Disease Control and Prevention · USEmory University · US

Funding

Spatio-temporal data integration methods for infectious disease surveillanceR01AI125842 · NIAID · UNIVERSITY OF CALIFORNIA BERKELEY · PI CHANG, HOWARD H, REMAIS, JUSTIN V · 2017 to 2021
$3.6M
NIAID NIH HHS R01 AI125842
6 · The paper itself

Abstract

In the United States, COVID-19 has become a leading cause of death since 2020. However, the number of COVID-19 deaths reported from death certificates is likely to represent an underestimate of the total deaths related to SARS-CoV-2 infections. Estimating those deaths not captured through death certificates is important to understanding the full burden of COVID-19 on mortality. In this work, we explored enhancements to an existing approach by employing Bayesian hierarchical models to estimate unrecognized deaths attributed to COVID-19 using weekly state-level COVID-19 viral surveillance and mortality data in the United States from March 2020 to April 2021. We demonstrated our model using those aged

Indexed as

Bayesian hierarchical modelingCOVID-19Excess mortalitySpatial–temporal modeling

Identifiers

PMID35013705
PMCPMC8730676
OpenAlexW4205375703

What OpenQuestion holds

Textmetadata
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.