Evidence map›Paper›PMID 40124614›Full record

ArticlePeerJ2025

Exploratory analysis of COVID-19 propagation using logistic model.

Xing Zhang, Chunhe Da, Wenjuan Ye

Abstract read
In one paragraph

Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Xing ZhangFirst Clinical Medical College, Gansu University of Chinese Medicine, Lanzhou, Gansu, China.
Chunhe DaFirst Clinical Medical College, Gansu University of Chinese Medicine, Lanzhou, Gansu, China.
Wenjuan YeFirst Clinical Medical College, Gansu University of Chinese Medicine, Lanzhou, Gansu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pandemics pose significant threats to social, economic, and public health. The novel coronavirus (COVID-19), which emerged in late 2019, quickly became a global public health crisis due to its high contagion and pathogenicity. Using data from the World Health Organization (WHO), this study applied the Logistic model to analyze the spread patterns of COVID-19 in 16 countries with over 10 million infections from 2020 to 2023. The findings reveal that as of December 31, 2023, global infections exceeded 772 million with over seven million deaths. The USA and China had the highest infection numbers, while Brazil had the highest mortality rate. The study identified three main outbreak patterns: initial, late, and gradual development, reflecting different stages of the pandemic. Countries with earlier outbreaks, such as India, Brazil, the USA, and Argentina, generally had higher mortality rates, while those with later outbreaks, such as China, Pakistan, Japan, and Australia, had lower mortality rates. Significant differences were observed in the duration and speed of the spread, with China showing the shortest average duration and Russia the longest. The Logistic model's parameter

Indexed as

COVID-19PandemicsDisease OutbreaksGlobal HealthHumansLogistic ModelsQuarantineSARS-CoV-2COVID-19Logistic modelNational policyPandemic transmission

Identifiers

PMID40124614
PMCPMC11927567

What OpenQuestion holds

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LicenceCC BY-NC
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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.