Evidence map›Paper›PMID 38914626›Full record

ArticleScientific reports2024

Differential impact of COVID-19 non-pharmaceutical interventions on the epidemiological dynamics of respiratory syncytial virus subtypes A and B.

Inga Holmdahl, Samantha J Bents, Rachel E Baker, Jean-Sebastien Casalegno, Nídia Sequeira Trovão, Sang Woo Park, Jessica E Metcalf, Cécile Viboud, Bryan Grenfell

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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.

Inga Holmdahl *Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
Samantha J Bents *Fogarty International Center, National Institutes of Health, Bethesda, MD, USA. sbents@alumni.princeton.edu.
Rachel E BakerSchool of Public Health, Brown University, Providence, RI, USA.
Jean-Sebastien CasalegnoHospices Civils de Lyon, Hôpital de la Croix-Rousse, Centre de Biologie Nord, Institut des Agents Infectieux, Laboratoire de Virologie, Lyon, France.
Nídia Sequeira TrovãoFogarty International Center, National Institutes of Health, Bethesda, MD, USA.
Sang Woo ParkDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
Jessica E MetcalfDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
Cécile ViboudFogarty International Center, National Institutes of Health, Bethesda, MD, USA.
Bryan GrenfellDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nonpharmaceutical interventions (NPIs) implemented during the COVID-19 pandemic have disrupted the dynamics of respiratory syncytial virus (RSV) on a global scale; however, the cycling of RSV subtypes in the pre- and post-pandemic period remains poorly understood. Here, we used a two subtype RSV model supplemented with epidemiological data to study the impact of NPIs on the two circulating subtypes, RSV-A and RSV-B. The model is calibrated to historic RSV subtype data from the United Kingdom and Finland and predicts a tendency for RSV-A dominance over RSV-B immediately following the implementation of NPIs. Using a global genetic dataset, we confirm that RSV-A has prevailed over RSV-B in the post-pandemic period, consistent with a higher R

Indexed as

COVID-19Respiratory Syncytial Virus, HumanRespiratory Syncytial Virus InfectionsSARS-CoV-2FinlandHumansInfantPandemicsUnited Kingdom

Identifiers

PMID38914626
PMCPMC11196647

What OpenQuestion holds

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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.