Evidence map›Paper›PMID 24149031›Full record

ArticleEmerging health threats journal2011

Use of media and public-domain Internet sources for detection and assessment of plant health threats.

Carla S Thomas, Noele P Nelson, Gary C Jahn, Tianchan Niu, David M Hartley

Open access · hybridAbstract read
In one paragraph

Article in Emerging health threats journal, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.9field-weighted citation impact, top 15% 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

4 citing papers in PubMed, 18 citations in OpenAlex.

  1. Article
  2. The persistent threat of emerging plant disease pandemics to global food security.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Review
  3. Article
  4. 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 at 2 institutions in 1 country.

Carla S ThomasDivision of Integrated Biodefense, Imaging Science and Information Systems, Georgetown University Medical Center, Washington, DC, USA.
Noele P Nelson
Gary C Jahn
Tianchan Niu
David M Hartley
Georgetown University · USGeorgetown University Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Event-based biosurveillance is a recognized approach to early warning and situational awareness of emerging health threats. In this study, we build upon previous human and animal health work to develop a new approach to plant pest and pathogen surveillance. We show that monitoring public domain electronic media for indications and warning of epidemics and associated social disruption can provide information about the emergence and progression of plant pest infestation or disease outbreak. The approach is illustrated using a case study, which describes a plant pest and pathogen epidemic in China and Vietnam from February 2006 to December 2007, and the role of ducks in contributing to zoonotic virus spread in birds and humans. This approach could be used as a complementary method to traditional plant pest and pathogen surveillance to aid global and national plant protection officials and political leaders in early detection and timely response to significant biological threats to plant health, economic vitality, and social stability. This study documents the inter-relatedness of health in human, animal, and plant populations and emphasizes the importance of plant health surveillance.

Indexed as

cropearly warningeconomic vitalityindicationsone healthplanthopperrice virussocial stabilitysurveillancewaterfowl

Identifiers

PMID24149031
PMCPMC3168368
OpenAlexW1966571045

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.