Evidence map›Paper›PMID 29065855›Full record

ArticleBMC infectious diseases2017

Spatio-temporal pattern analysis for evaluation of the spread of human infections with avian influenza A(H7N9) virus in China, 2013-2014.

Wen Dong, Kun Yang, Quanli Xu, Lin Liu, Juan Chen

Abstract read
In one paragraph

Article in BMC infectious diseases, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–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

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  9. Article
  10. Spatiotemporal Analysis for COVID-19 Delta Variant Using GIS-Based Air Parameter and Spatial Modeling.International journal of environmental research and public health · 2022
    Article
  11. Article
  12. Article
  13. H7N9 Influenza Virus in China.Cold Spring Harbor perspectives in medicine · 2021
    Review
  14. Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China.International journal of environmental research and public health · 2020
    Article
  15. Article
  16. Article
  17. Spatiotemporal Variation and Hotspot Detection of the Avian Influenza A(H7N9) Virus in China, 2013⁻2017.International journal of environmental research and public health · 2019
    Article
  18. 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

5 authors.

Wen DongSchool of Information Science and Technology, Yunnan Normal University, Kunming, Yunnan, China.
Kun YangSchool of Information Science and Technology, Yunnan Normal University, Kunming, Yunnan, China. kmdcynu@163.com.
Quanli XuSchool of Tourism and Geographic Science, Yunnan Normal University, Kunming, Yunnan, China.
Lin LiuSchool of Information Science and Technology, Yunnan Normal University, Kunming, Yunnan, China.
Juan ChenSchool of Information Science and Technology, Yunnan Normal University, Kunming, Yunnan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA large number (n = 460) of A(H7N9) human infections have been reported in China from March 2013 through December 2014, and H7N9 outbreaks in humans became an emerging issue for China health, which have caused numerous disease outbreaks in domestic poultry and wild bird populations, and threatened human health severely. The aims of this study were to investigate the directional trend of the epidemic and to identify the significant presence of spatial-temporal clustering of influenza A(H7N9) human cases between March 2013 and December 2014.

methodsThree distinct epidemic phases of A(H7N9) human infections were identified in this study. In each phase, standard deviational ellipse analysis was conducted to examine the directional trend of disease spreading, and retrospective space-time permutation scan statistic was then used to identify the spatio-temporal cluster patterns of H7N9 outbreaks in humans.

resultsThe ever-changing location and the increasing size of the three identified standard deviational ellipses showed that the epidemic moved from east to southeast coast, and hence to some central regions, with a future epidemiological trend of continue dispersing to more central regions of China, and a few new human cases might also appear in parts of the western China. Furthermore, A(H7N9) human infections were clustering in space and time in the first two phases with five significant spatio-temporal clusters (p < 0.05), but there was no significant cluster identified in phase III.

conclusionsThere was a new epidemiologic pattern that the decrease in significant spatio-temporal cluster of A(H7N9) human infections was accompanied with an obvious spatial expansion of the outbreaks during the study period, and identification of the spatio-temporal patterns of the epidemic can provide valuable insights for better understanding the spreading dynamics of the disease in China.

Indexed as

Spatio-Temporal AnalysisAnimalsBirdsChinaCluster AnalysisDisease OutbreaksHumansInfluenza A Virus, H7N9 SubtypeInfluenza, HumanInfluenza in BirdsPhylogenyRetrospective StudiesA(H7N9) human infectionsDirectional trendSpatio-temporal clustersSpreading pattern

Identifiers

PMID29065855
PMCPMC5655814

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LicenceCC BY
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Registered trials

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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.