Evidence map›Paper›PMID 23918033›Full record

ReviewEcoHealth2013

Deciphering serology to understand the ecology of infectious diseases in wildlife.

Amy T Gilbert, A R Fooks, D T S Hayman, D L Horton, T Müller, R Plowright, A J Peel, R Bowen, J L N Wood, J Mills and 2 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in EcoHealth, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
111citing papers in PubMed, 5 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

111 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
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  4. Prioritizing surveillance of Nipah virus in India.PLoS neglected tropical diseases · 2019
    Pooled it
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  6. Trial
  7. Article
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  12. Metagenomic survey reveals Volzhskoe tick virus inOne health (Amsterdam, Netherlands) · 2025
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  19. Article
  20. Review

51 more citing papers are in PubMed but not listed here.

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

12 authors.

Amy T GilbertNational Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA, 30333, USA, Amy.T.Gilbert@aphis.usda.gov.
A R Fooks
D T S Hayman
D L Horton
T Müller
R Plowright
A J Peel
R Bowen
J L N Wood
J Mills
A A Cunningham
C E Rupprecht

Funding

Wellcome Trust
6 · The paper itself

Abstract

The ecology of infectious disease in wildlife has become a pivotal theme in animal and public health. Studies of infectious disease ecology rely on robust surveillance of pathogens in reservoir hosts, often based on serology, which is the detection of specific antibodies in the blood and is used to infer infection history. However, serological data can be inaccurate for inference to infection history for a variety of reasons. Two major aspects in any serological test can substantially impact results and interpretation of antibody prevalence data: cross-reactivity and cut-off thresholds used to discriminate positive and negative reactions. Given the ubiquitous use of serology as a tool for surveillance and epidemiological modeling of wildlife diseases, it is imperative to consider the strengths and limitations of serological test methodologies and interpretation of results, particularly when using data that may affect management and policy for the prevention and control of infectious diseases in wildlife. Greater consideration of population age structure and cohort representation, serological test suitability and standardized sample collection protocols can ensure that reliable data are obtained for downstream modeling applications to characterize, and evaluate interventions for, wildlife disease systems.

Indexed as

EcologyAnimalsAntibodiesCommunicable DiseasesCross ReactionsDifferential ThresholdDisease ReservoirsEpidemiological MonitoringIncidencePrevalenceReproducibility of ResultsSerologic TestsAntibodies

Identifiers

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.