Evidence map›Paper›PMID 36844759›Full record

ArticleInfectious Disease Modelling2023

Comparing the transmission potential from sequence and surveillance data of 2009 North American influenza pandemic waves.

Venkata R Duvvuri, Joseph T Hicks, Lambodhar Damodaran, Martin Grunnill, Thomas Braukmann, Jianhong Wu, Jonathan B Gubbay, Samir N Patel, Justin Bahl

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Infectious Disease Modelling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

9 authors.

Venkata R DuvvuriPublic Health Ontario, Toronto, Ontario, Canada.
Joseph T HicksCenter for the Ecology of Infectious Disease, Department of Infectious Diseases, Institute of Bioinformatics, University of Georgia, Athens, Georgia.
Lambodhar DamodaranCenter for the Ecology of Infectious Disease, Department of Infectious Diseases, Institute of Bioinformatics, University of Georgia, Athens, Georgia.
Martin GrunnillPublic Health Ontario, Toronto, Ontario, Canada.
Thomas BraukmannPublic Health Ontario, Toronto, Ontario, Canada.
Jianhong WuLaboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada.
Jonathan B GubbayPublic Health Ontario, Toronto, Ontario, Canada.
Samir N PatelPublic Health Ontario, Toronto, Ontario, Canada.
Justin BahlCenter for the Ecology of Infectious Disease, Department of Infectious Diseases, Institute of Bioinformatics, University of Georgia, Athens, Georgia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Technological advancements in phylodynamic modeling coupled with the accessibility of real-time pathogen genetic data are increasingly important for understanding the infectious disease transmission dynamics. In this study, we compare the transmission potentials of North American influenza A(H1N1)pdm09 derived from sequence data to that derived from surveillance data. The impact of the choice of tree-priors, informative epidemiological priors, and evolutionary parameters on the transmission potential estimation is evaluated. North American Influenza A(H1N1)pdm09 hemagglutinin (HA) gene sequences are analyzed using the coalescent and birth-death tree prior models to estimate the basic reproduction number (

Indexed as

Birth-death modelsCoalescent growth modelsPandemic 2009 H1N1Pathogen sequence dataPhylodynamicsPublic healthReproduction number

Identifiers

PMID36844759
PMCPMC9944206

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.