Evidence map›Paper›PMID 34002048›Full record

ReviewNature ecology & evolution2021

Towards an ecosystem model of infectious disease.

James M Hassell, Tim Newbold, Andrew P Dobson, Yvonne-Marie Linton, Lydia H V Franklinos, Dawn Zimmerman, Katrina M Pagenkopp Lohan

Erratum issuedAbstract readReview
PubMed Publisher
In one paragraph

Review in Nature ecology & evolution, 2021. 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 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
7.8field-weighted citation impact, top 2% 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

22 citing papers in PubMed, 53 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 5 institutions in 2 countries.

James M HassellGlobal Health Program, Smithsonian Conservation Biology Institute, Washington DC, USA. hasselljm@si.edu.ORCID http://orcid.org/0000-0002-4710-2827
Tim NewboldCentre for Biodiversity & Environment Research (CBER), Department of Genetics, Evolution and Environment, University College London, London, UK.ORCID http://orcid.org/0000-0001-7361-0051
Andrew P DobsonDepartment of Ecology & Evolutionary Biology, Princeton University, Princeton, NJ, USA.ORCID http://orcid.org/0000-0002-9678-1694
Yvonne-Marie LintonWalter Reed Biosystematics Unit (WRBU), Smithsonian Institution Museum Support Center, Suitland, MD, USA.
Lydia H V FranklinosCentre for Biodiversity & Environment Research (CBER), Department of Genetics, Evolution and Environment, University College London, London, UK.
Dawn ZimmermanGlobal Health Program, Smithsonian Conservation Biology Institute, Washington DC, USA.
Katrina M Pagenkopp LohanMarine Disease Ecology Laboratory, Smithsonian Environmental Research Center, Edgewater, MD, USA.
Smithsonian Institution · USUniversity College London · GBSanta Fe Institute · USSmithsonian Conservation Biology Institute · USSmithsonian Environmental Research Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Increasingly intimate associations between human society and the natural environment are driving the emergence of novel pathogens, with devastating consequences for humans and animals alike. Prior to emergence, these pathogens exist within complex ecological systems that are characterized by trophic interactions between parasites, their hosts and the environment. Predicting how disturbance to these ecological systems places people and animals at risk from emerging pathogens-and the best ways to manage this-remains a significant challenge. Predictive systems ecology models are powerful tools for the reconstruction of ecosystem function but have yet to be considered for modelling infectious disease. Part of this stems from a mistaken tendency to forget about the role that pathogens play in structuring the abundance and interactions of the free-living species favoured by systems ecologists. Here, we explore how developing and applying these more complete systems ecology models at a landscape scale would greatly enhance our understanding of the reciprocal interactions between parasites, pathogens and the environment, placing zoonoses in an ecological context, while identifying key variables and simplifying assumptions that underly pathogen host switching and animal-to-human spillover risk. As well as transforming our understanding of disease ecology, this would also allow us to better direct resources in preparation for future pandemics.

Indexed as

Communicable DiseasesEcosystemAnimalsHumansZoonoses

Identifiers

PMID34002048
OpenAlexW3163024822

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.