Evidence map›Paper›PMID 40832395›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Characterizing the informativeness of pathogen genome sequence datasets about transmission between population groups.

Cécile Tran-Kiem, Amanda C Perofsky, Justin Lessler, Trevor Bedford

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Cécile Tran-KiemVaccine and Infectious Diseases Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.ORCID 0000-0003-0563-8428
Amanda C PerofskyFogarty International Center, US National Institutes of Health, Bethesda, MD, USA.
Justin LesslerUniversity of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Trevor BedfordVaccine and Infectious Diseases Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.

Funding

Real-time tracking of virus evolution for vaccine strain selection and epidemiological investigationR35GM119774 · NIGMS · FRED HUTCHINSON CANCER RESEARCH CENTER · PI BEDFORD, TREVOR BC · 2016 to 2025
$4.1M
NIGMS NIH HHS R35 GM119774
6 · The paper itself

Abstract

Pathogen genome analysis helps characterize transmission between population groups. The information carried by pathogen sequences comes from the accumulation of mutations within their genomes; thus, that the pace at which mutations accumulate should determine the granularity of transmission processes that pathogen sequences can characterize. Here, we investigate how the complex interplay between mutation, transmission, population mixing and sampling impacts the power of phylogeographic studies. First, we develop a conceptual probabilistic framework to quantify the ability of pairs of sequences in capturing migration history. This allows us to comprehensively explore the space of possible phylogeographic analyses by explicitly considering the pace at which mutations accumulate and the pace at which migration events occur. Using this framework, we identify a pathogen-intrinsic limit in the mixing scale at which their sequence data remains informative, with faster mutating pathogens enabling finer spatial characterization. Secondly, we perform a simulation study exploring a range of assumptions regarding sequencing intensity. We find that sample size further imposes a limit on the characterization of mixing processes. This work highlights inherent horizons of observability for population mixing processes that depend on the interaction between evolution, transmission, mixing and sampling. Such considerations are important for the design of phylogeographic studies.

Identifiers

PMID40832395
PMCPMC12363720

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.