Evidence map›Paper›PMID 28376125›Full record

ArticlePloS one2017

Influenza A H5N1 and H7N9 in China: A spatial risk analysis.

Chau Minh Bui, Lauren Gardner, Raina MacIntyre, Sahotra Sarkar

Erratum issuedAbstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

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  10. Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020).International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2020
    Article
  11. Article
  12. Identifying Risk Factors Of A(H7N9) Outbreak by Wavelet Analysis and Generalized Estimating Equation.International journal of environmental research and public health · 2019
    Article
  13. Article
  14. Article
  15. Avian influenza overview October 2016-August 2017.EFSA journal. European Food Safety Authority · 2017
    Article
  16. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Chau Minh BuiSchool of Public Health and Community Medicine, University of New South Wales, Sydney, New South Wales, Australia.ORCID http://orcid.org/0000-0001-9046-5810
Lauren GardnerSchool of Civil and Environmental Engineering, University of New South Wales, Sydney, New South Wales, Australia.
Raina MacIntyreSchool of Public Health and Community Medicine, University of New South Wales, Sydney, New South Wales, Australia.
Sahotra SarkarDepartments of Philosophy and Integrative Biology, University of Texas, Austin, Texas, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundZoonotic avian influenza poses a major risk to China, and other parts of the world. H5N1 has remained endemic in China and globally for nearly two decades, and in 2013, a novel zoonotic influenza A subtype H7N9 emerged in China. This study aimed to improve upon our current understanding of the spreading mechanisms of H7N9 and H5N1 by generating spatial risk profiles for each of the two virus subtypes across mainland China. METHODS AND

findingsIn this study, we (i) developed a refined data set of H5N1 and H7N9 locations with consideration of animal/animal environment case data, as well as spatial accuracy and precision; (ii) used this data set along with environmental variables to build species distribution models (SDMs) for each virus subtype in high resolution spatial units of 1km2 cells using Maxent; (iii) developed a risk modelling framework which integrated the results from the SDMs with human and chicken population variables, which was done to quantify the risk of zoonotic transmission; and (iv) identified areas at high risk of H5N1 and H7N9 transmission. We produced high performing SDMs (6 of 8 models with AUC > 0.9) for both H5N1 and H7N9. In all our SDMs, H7N9 consistently showed higher AUC results compared to H5N1, suggesting H7N9 suitability could be better explained by environmental variables. For both subtypes, high risk areas were primarily located in south-eastern China, with H5N1 distributions found to be more diffuse and extending more inland compared to H7N9.

conclusionsWe provide projections of our risk models to public health policy makers so that specific high risk areas can be targeted for control measures. We recommend comparing H5N1 and H7N9 prevalence rates and survivability in the natural environment to better understand the role of animal and environmental transmission in human infections.

Indexed as

Influenza A Virus, H5N1 SubtypeInfluenza A Virus, H7N9 SubtypeAnimalsBirdsChinaDisease OutbreaksEndemic DiseasesHumansInfluenza, HumanInfluenza in BirdsModels, BiologicalRisk FactorsSpatial AnalysisZoonoses

Identifiers

PMID28376125
PMCPMC5380336

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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.