Evidence map›Paper›PMID 39432551›Full record

ArticlePLoS pathogens2024

Features that matter: Evolutionary signatures can predict viral transmission routes.

Maya Wardeh, Jack Pilgrim, Melody Hui, Aurelia Kotsiri, Matthew Baylis, Marcus S C Blagrove

Abstract read
In one paragraph

Article in PLoS pathogens, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Maya WardehDepartment of Computer Science, University of Liverpool, Liverpool, United Kingdom.ORCID 0000-0002-2316-5460
Jack PilgrimInstitute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom.
Melody HuiInstitute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom.
Aurelia KotsiriInstitute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom.
Matthew BaylisInstitute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom.ORCID 0000-0003-0335-187X
Marcus S C BlagroveInstitute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom.

Funding

Biotechnology and Biological Sciences Research Council (BBSRC)Medical Research Council (MRC)Natural Environment Research Council (NERC)
6 · The paper itself

Abstract

Routes of virus transmission between hosts are key to understanding viral epidemiology. Different routes have large effects on viral ecology, and likelihood and rate of transmission; for example, respiratory and vector-borne viruses together encompass the majority of rapid outbreaks and high-consequence animal and plant epidemics. However, determining the specific transmission route(s) can take months to years, delaying mitigation efforts. Here, we identify the viral features and evolutionary signatures which are predictive of viral transmission routes and use them to predict potential routes for fully-sequenced viruses in silico and rapidly, for both viruses with no observed routes, as well as viruses with missing routes. This was achieved by compiling a dataset of 24,953 virus-host associations with 81 defined transmission routes, constructing a hierarchy of virus transmission encompassing those routes and 42 higher-order modes, and engineering 446 predictive features from three complementary perspectives. We integrated those data and features to train 98 independent ensembles of LightGBM classifiers. We found that all features contributed to the prediction for at least one of the routes and/or modes of transmission, demonstrating the utility of our broad multi-perspective approach. Our framework achieved ROC-AUC = 0.991, and F1-score = 0.855 across all included transmission routes and modes, and was able to achieve high levels of predictive performance for high-consequence respiratory (ROC-AUC = 0.990, and F1-score = 0.864) and vector-borne transmission (ROC-AUC = 0.997, and F1-score = 0.921). Our framework ranks the viral features in order of their contribution to prediction, per transmission route, and hence identifies the genomic evolutionary signatures associated with each route. Together with the more matured field of viral host-range prediction, our predictive framework could: provide early insights into the potential for, and pattern of viral spread; facilitate rapid response with appropriate measures; and significantly triage the time-consuming investigations to confirm the likely routes of transmission.

Indexed as

Virus DiseasesAnimalsBiological EvolutionEvolution, MolecularHumansViruses

Identifiers

PMID39432551
PMCPMC11527288

What OpenQuestion holds

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LicenceCC BY
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.