Evidence map›Paper›PMID 34458547›Full record

ArticleOne health (Amsterdam, Netherlands)2021

Assessing different interventions against Avian Influenza A (H7N9) infection by an epidemiological model.

Xueli Wei, Liying Wang, Qiaojuan Jia, Jianpeng Xiao, Guanghu Zhu

Abstract read
In one paragraph

Article in One health (Amsterdam, Netherlands), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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.

Xueli WeiSchool of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, China.
Liying WangSchool of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, China.
Qiaojuan JiaSchool of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, China.
Jianpeng XiaoGuangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China.
Guanghu ZhuSchool of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper aims at evaluating the effectiveness of different intervention measures against the infection of avian influenza A (H7N9) by using an epidemiological model. The model formulates the intrinsic interactions of domestic poultry (DP), H7N9 virus and humans by ordinary differential equations and couples the essential roles of various interventions (including culling, vaccinating, screening, disinfecting, and reducing contact rate, etc). Qualitative analysis indicates that when the recruiting poultry is virus-free, there is a transmission threshold denoted by basic reproduction number which can determine the invasion of H7N9; and there is always a stable H7N9 endemic in case of persistent import of virus-carrying poultry, under which only complete vaccination or cutting off poultry-to-poultry/human contacts can stop H7N9 transmission. By performing numerical analysis of the model with biological background parameters, the intervention outcomes against H7N9 infection are further quantified. It is found that (1) reducing poultry-human/poultry interaction and per-contact infection probability, as well as culling DP, are highly effective in diminishing the infections of humans and DP; (2) the disease is prevented when larger than (1 - 0.1

Indexed as

basic reproduction numberH7N9interventiontransmission dynamics

Identifiers

PMID34458547
PMCPMC8379632

What OpenQuestion holds

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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.