Evidence map›Paper›PMID 36714536›Full record

ReviewOne health (Amsterdam, Netherlands)2023

Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities.

Francisca Astorga, Quentin Groom, Paloma Helena Fernandes Shimabukuro, Sylvie Manguin, Daniel Noesgaard, Thomas Orrell, Marianne Sinka, Tim Hirsch, Dmitry Schigel

Abstract readReview
In one paragraph

Review in One health (Amsterdam, Netherlands), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Globally aggregated biodiversity data impact predictive and descriptive research.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  4. Article
  5. Article
  6. Pathogens and planetary change.Nature reviews. Biodiversity · 2025
    Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Hyperparameter optimization for cardiovascular disease data-driven prognostic system.Visual computing for industry, biomedicine, and art · 2023
    Article
  13. Review
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

9 authors.

Francisca AstorgaFacultad de Ciencias, Universidad Mayor, Chile.
Quentin GroomBiodiversity Informatics, Meise Botanic Garden, Belgium Nieuwelaan 38, 1860, Meise, Belgium.
Paloma Helena Fernandes ShimabukuroInstituto René Rachou, Fundação Oswaldo Cruz, Brazil Av. Augusto de Lima, 1715 - Barro Preto, Belo Horizonte, MG 30190-002, Brazil.
Sylvie ManguinHSM, University Montpellier, CNRS, IRD, 911 Av. Agropolis, 34394 Montpellier, France.
Daniel NoesgaardGlobal Biodiversity Information Facility, Universitetsparken 15, DK-2100 Copenhagen Ø, Denmark.
Thomas OrrellSmithsonian Institution, National Museum of Natural History, 10th St. & Constitution Ave. NW, Washington, DC 20560, USA.
Marianne SinkaUniversity of Oxford, Oxford OX1 2JD, United Kingdom.
Tim HirschGlobal Biodiversity Information Facility, Universitetsparken 15, DK-2100 Copenhagen Ø, Denmark.
Dmitry SchigelGlobal Biodiversity Information Facility, Universitetsparken 15, DK-2100 Copenhagen Ø, Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The unprecedented generation of large volumes of biodiversity data is consistently contributing to a wide range of disciplines, including disease ecology. Emerging infectious diseases are usually zoonoses caused by multi-host pathogens. Therefore, their understanding may require the access to biodiversity data related to the ecology and the occurrence of the species involved. Nevertheless, despite several data-mobilization initiatives, the usage of biodiversity data for research into disease dynamics has not yet been fully leveraged. To explore current contribution, trends, and to identify limitations, we characterized biodiversity data usage in scientific publications related to human health, contrasting patterns of studies citing the Global Biodiversity Information Facility (GBIF) with those obtaining data from other sources. We found that the studies mainly obtained data from scientific literature and other not aggregated or standardized sources. Most of the studies explored pathogen species and, particularly those with GBIF-mediated data, tended to explore and reuse data of multiple species (>2). Data sources varied according to the taxa and epidemiological roles of the species involved. Biodiversity data repositories were mainly used for species related to hosts, reservoirs, and vectors, and barely used as a source of pathogens data, which was usually obtained from human and animal-health related institutions. While both GBIF- and not GBIF-mediated data studies explored similar diseases and topics, they presented discipline biases and different analytical approaches. Research on emerging infectious diseases may require the access to geographical and ecological data of multiple species. The One Health challenge requires interdisciplinary collaboration and data sharing, which is facilitated by aggregated repositories and platforms. The contribution of biodiversity data to understand infectious disease dynamics should be acknowledged, strengthened, and promoted.

Indexed as

BioinformaticsGBIFGBIF, Global Biodiversity Information FacilityRisk assessmentVector-borne diseasesZoonoses

Identifiers

PMID36714536
PMCPMC9880238

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.