Evidence map›Paper›PMID 32339715›Full record

ArticleInternational journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases2020

Statistical and network analysis of 1212 COVID-19 patients in Henan, China.

Pei Wang, Jun-An Lu, Yanyu Jin, Mengfan Zhu, Lingling Wang, Shunjie Chen

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 3 pooled it
15.1field-weighted citation impact, top 1% 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

36 citing papers in PubMed, 3 syntheses or guidelines pooled it, 91 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Statistical Identification of Important Nodes in Biological Systems.Journal of systems science and complexity · 2021
    Article
  18. Review
  19. Review
  20. 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

6 authors at 3 institutions in 1 country.

Pei WangSchool of Mathematics and Statistics, Henan University, Kaifeng, 475004, China; Institute of Applied Mathematics, Henan University, Kaifeng, 475004, China; Laboratory of Data Analysis Technology, Henan University, 475004, Kaifeng, China. Electronic address: wp0307@126.com.
Jun-An LuSchool of Mathematics and Statistics, Wuhan University, Wuhan, 430070, China. Electronic address: jalu@whu.edu.cn.
Yanyu JinSchool of Mathematics and Statistics, Henan University, Kaifeng, 475004, China.
Mengfan ZhuSchool of Mathematics and Statistics, Zhongnan University of Economics and Law, Wuhan, 430073, China.
Lingling WangSchool of Mathematics and Statistics, Henan University, Kaifeng, 475004, China.
Shunjie ChenSchool of Mathematics and Statistics, Henan University, Kaifeng, 475004, China.
Henan University · CNWuhan University · CNZhongnan University of Economics and Law · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCOVID-19 is spreading quickly all over the world. Publicly released data for 1212 COVID-19 patients in Henan of China were analyzed in this paper.

methodsVarious statistical and network analysis methods were employed.

resultsWe found that COVID-19 patients show gender (55% vs 45%) and age (81% aged between 21 and 60) preferences; possible causes were explored. The estimated average, mode and median incubation periods are 7.4, 4 and 7 days. Incubation periods of 92% of patients were no more than 14 days. The epidemic in Henan has undergone three stages and has shown high correlations with the numbers of patients recently returned from Wuhan. Network analysis revealed that 208 cases were clustering infected, and various People's Hospitals are the main force in treating COVID-19.

conclusionsThe incubation period was statistically estimated, and the proposed state transition diagram can explore the epidemic stages of emerging infectious disease. We suggest that although the quarantine measures are gradually working, strong measures still might be needed for a period of time, since ∼7.45% of patients may have very long incubation periods. Migrant workers or college students are at high risk. State transition diagrams can help us to recognize the time-phased nature of the epidemic. Our investigations have implications for the prevention and control of COVID-19 in other regions of the world.

Indexed as

BetacoronavirusAdultChinaCoronavirus InfectionsCOVID-19FemaleHumansMaleMiddle AgedPandemicsPneumonia, ViralSARS-CoV-2Young AdultAggregate outbreakCOVID-19Henan provinceIncubation periodNetwork analysisTime-phased nature of epidemic

Identifiers

PMID32339715
PMCPMC7180361
OpenAlexW3020049202

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.