Evidence map›Paper›PMID 34683095›Full record

ArticleJournal of personalized medicine2021

Graphical Trajectory Comparison to Identify Errors in Data of COVID-19: A Cross-Country Analysis.

Lan Yao, Wei Dong, Jim Y Wan, Scott C Howard, Minghui Li, Joyce Carolyn Graff

Abstract read
In one paragraph

Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. COVID-19 Pandemic Risk Assessment: Systematic Review.Risk management and healthcare policy · 2024
    Review
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

6 authors.

Lan YaoHealth Outcomes and Policy Research, College of Graduate Health Sciences, University of Tennessee Health Science Center, Memphis, TN 38163, USA.
Wei DongCollege of Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA.
Jim Y WanCollege of Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA.
Scott C HowardCollege of Nursing, University of Tennessee Health Science Center, Memphis, TN 38163, USA.
Minghui LiCollege of Pharmacy, University of Tennessee Health Science Center, Memphis, TN 38163, USA.
Joyce Carolyn GraffCollege of Nursing, University of Tennessee Health Science Center, Memphis, TN 38163, USA.

Funding

Health Science Center, University of Tennessee faculty funding
6 · The paper itself

Abstract

Data from the early stage of a novel infectious disease outbreak provide vital information in risk assessment, prediction, and precise disease management. Since the first reported case of COVID-19, the pattern of the novel coronavirus transmission in Wuhan has become the interest of researchers in epidemiology and public health. To thoroughly map the mechanism of viral spreading, we used the patterns of data at the early onset of COVID-19 from seven countries to estimate the time lag between peak days of cases and deaths. This study compared these data with those of Wuhan and estimated the natural history of disease across the infected population and the time lag. The findings suggest that comparative analyses of data from different regions and countries reveal the differences between peaks of cases and deaths caused by COVID-19 and the incomplete and underestimated cases in Wuhan. Different countries may show different patterns of cases peak days, deaths peak days, and peak periods. Error in the early COVID-19 statistics in Brazil was identified. This study provides sound evidence for policymakers to understand the local circumstances in diagnosing the health of a population and propose precise and timely public health interventions to control and prevent infectious diseases.

Indexed as

COVID-19death ratedisease patternerroronsettime lag

Identifiers

PMID34683095
PMCPMC8537769

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.