Evidence map›Paper›PMID 38503333›Full record

ArticleProceedings. Biological sciences2024

Congruity of genomic and epidemiological data in modelling of local cholera outbreaks.

Mateusz Wilinski, Lauren Castro, Jeffrey Keithley, Carrie Manore, Josefina Campos, Ethan Romero-Severson, Daryl Domman, Andrey Y Lokhov

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings. Biological sciences, 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

8 authors.

Mateusz WilinskiTheoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.ORCID 0000-0002-8539-1586
Lauren CastroAnalytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
Jeffrey KeithleyAnalytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
Carrie ManoreTheoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.ORCID 0000-0003-2534-9316
Josefina CamposUO Centro Nacional de Genómica y Bioinformtica, ANLIS 'Dr. Carlos G. Malbrán, Buenos Aires, Argentina.
Ethan Romero-SeversonTheoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
Daryl DommanCenter for Global Health, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM, USA.
Andrey Y LokhovTheoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.

Funding

Reconstructing HIV Epidemics from HIV PhylogeneticsR01AI087520 · NIAID · TRIAD NATIONAL SECURITY, LLC · PI Thomas K. Leitner · 2010 to 2026
$11.2M
University of New Mexico Clinical and Translational Science CenterKL2TR001448 · NCATS · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI CANNON, JUDY LIN · 2015 to 2024
$3.5M
NCATS NIH HHS KL2 TR001448NIAID NIH HHS R01 AI087520
6 · The paper itself

Abstract

Cholera continues to be a global health threat. Understanding how cholera spreads between locations is fundamental to the rational, evidence-based design of intervention and control efforts. Traditionally, cholera transmission models have used cholera case-count data. More recently, whole-genome sequence data have qualitatively described cholera transmission. Integrating these data streams may provide much more accurate models of cholera spread; however, no systematic analyses have been performed so far to compare traditional case-count models to the phylodynamic models from genomic data for cholera transmission. Here, we use high-fidelity case-count and whole-genome sequencing data from the 1991 to 1998 cholera epidemic in Argentina to directly compare the epidemiological model parameters estimated from these two data sources. We find that phylodynamic methods applied to cholera genomics data provide comparable estimates that are in line with established methods. Our methodology represents a critical step in building a framework for integrating case-count and genomic data sources for cholera epidemiology and other bacterial pathogens.

Indexed as

CholeraEpidemicsDisease OutbreaksGenomicsHumansWhole Genome Sequencingcholeraepidemic modellinggenomic data‌model selection

Identifiers

PMID38503333
PMCPMC10950457

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Registered trials

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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.