Evidence map›Paper›PMID 37873325›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Inferring Viral Transmission Pathways from Within-Host Variation.

Ivan O A Specht, Brittany A Petros, Gage K Moreno, Taylor Brock-Fisher, Lydia A Krasilnikova, Mark Schifferli, Katherine Yang, Paul Cronan, Olivia Glennon, Stephen F Schaffner and 7 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. 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

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

17 authors.

Ivan O A SpechtThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Brittany A PetrosThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Gage K MorenoThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Taylor Brock-FisherThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Lydia A KrasilnikovaThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Mark SchifferliFathom Information Design, Boston, MA 02114, USA.
Katherine YangFathom Information Design, Boston, MA 02114, USA.
Paul CronanFathom Information Design, Boston, MA 02114, USA.
Olivia GlennonFathom Information Design, Boston, MA 02114, USA.
Stephen F SchaffnerThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Daniel J ParkThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Bronwyn L MacInnisThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Al OzonoffThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Ben FryFathom Information Design, Boston, MA 02114, USA.
Michael D MitzenmacherDepartment of Computer Science, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.
Patrick VarillyThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Pardis C SabetiThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.

Funding

Viral Genomics: evolution, spread, and host interactionsU19AI110818 · NIAID · BROAD INSTITUTE, INC. · PI NEAFSEY, DANIEL E · 2014 to 2024
$66.5M
Medical Scientist Training ProgramT32GM007753 · NIGMS · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI WALENSKY, LOREN DAVID · 1985 to 2021
$50.0M
Medical Scientist Training ProgramT32GM144273 · NIGMS · HARVARD MEDICAL SCHOOL · PI David Shumway Jones, Jacqueline A. Lees · 2022 to 2026
$14.7M
West African Emerging Infectious Disease Research Center (WA-EIDRC)U01AI151812 · NIAID · SCRIPPS RESEARCH INSTITUTE, THE · PI ANDERSEN, KRISTIAN GRAUGAARD, GARRY, ROBERT F · 2020 to 2024
$13.2M
NIAID NIH HHS U01 AI151812NIAID NIH HHS U19 AI110818NIGMS NIH HHS T32 GM007753NIGMS NIH HHS T32 GM144273
6 · The paper itself

Abstract

Genome sequencing can offer critical insight into pathogen spread in viral outbreaks, but existing transmission inference methods use simplistic evolutionary models and only incorporate a portion of available genetic data. Here, we develop a robust evolutionary model for transmission reconstruction that tracks the genetic composition of within-host viral populations over time and the lineages transmitted between hosts. We confirm that our model reliably describes within-host variant frequencies in a dataset of 134,682 SARS-CoV-2 deep-sequenced genomes from Massachusetts, USA. We then demonstrate that our reconstruction approach infers transmissions more accurately than two leading methods on synthetic data, as well as in a controlled outbreak of bovine respiratory syncytial virus and an epidemiologically-investigated SARS-CoV-2 outbreak in South Africa. Finally, we apply our transmission reconstruction tool to 5,692 outbreaks among the 134,682 Massachusetts genomes. Our methods and results demonstrate the utility of within-host variation for transmission inference of SARS-CoV-2 and other pathogens, and provide an adaptable mathematical framework for tracking within-host evolution.

Identifiers

PMID37873325
PMCPMC10593003

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.