Evidence map›Paper›PMID 41259406›Full record

ArticlePLoS computational biology2025

Clinical progression parameters associated with SARS-CoV-2, influenza, and respiratory syncytial virus infections in a large US integrated healthcare population.

Noah T Parker, Vennis Hong, Gregg S Davis, Magdalena Pomichowski, Iris A Reyes, Fagen Xie, Nicola F Mueller, Isabel Rodriguez-Barraquer, Sara Y Tartof, Joseph A Lewnard

Abstract read
In one paragraph

Article in PLoS computational biology, 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

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

10 authors.

Noah T ParkerDepartment of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
Vennis HongDepartment of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States of America.
Gregg S DavisDepartment of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States of America.
Magdalena PomichowskiDepartment of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States of America.
Iris A ReyesDepartment of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States of America.
Fagen XieDepartment of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States of America.
Nicola F MuellerDivision of HIV, Infectious Diseases, and Global Medicine, University of California, San Francisco, San Francisco, California, United States of America.
Isabel Rodriguez-BarraquerDivision of HIV, Infectious Diseases, and Global Medicine, University of California, San Francisco, San Francisco, California, United States of America.
Sara Y TartofDepartment of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States of America.ORCID 0000-0003-2336-8476
Joseph A LewnardSchool of Public Health, University of California, Berkeley, Berkeley, California, United States of America.ORCID 0000-0002-5417-8475

Funding

US Centers for Disease Control CDC-RFA-FT-23-0069
6 · The paper itself

Abstract

Mathematical and computational models are often used to forecast respiratory infectious disease burden, including to inform healthcare capacity. We aimed to characterize pathways of clinical progression associated with SARS-CoV-2, influenza, and respiratory syncytial virus (RSV) infections using data from patients aged 0 to >90 years in an integrated healthcare system, whose encounters were monitored across all levels of acuity spanning virtual, ambulatory, and inpatient care settings. Using parametric survival models, we estimated probabilities of progression and distributions of time to progression from each setting to all higher-acuity settings on a cascade encompassing the following classes of events or encounters: symptoms onset; diagnostic testing; telehealth or other virtual care appointment; outpatient physician office visit; urgent care presentation; emergency department presentation; hospital admission; mechanical ventilation; and death. Our analyses included data from 59,668, 22,705, and 1,668 episodes associated with positive SARS-CoV-2, influenza, and RSV tests, respectively, between 1 April 2023 and 31 March 2024. First clinical encounters occurred in inpatient settings for only 4.7%, 3.4%, and 18.7% of SARS-CoV-2, influenza, and RSV episodes, respectively, with median times (interquartile range) of 6.8 (3.6-13.2), 6.6 (3.5-12.1), and 6.4 (3.8-10.6) days from symptoms onset to admission. Overall, 7.9% of SARS-CoV-2 episodes, 5.8% of influenza episodes, and 33.8% of RSV episodes resulted in inpatient admission, ventilation, or death. Between 40.4-62.1%, 71.6-87.3%, and 47.9-58.7% of SARS-CoV-2, influenza, and RSV infections, respectively, had encounters in lower-acuity virtual care, outpatient, or urgent care settings. For all three viruses, the proportions of cases receiving care at each level of acuity increased with older age and greater numbers of comorbid conditions. Median durations of hospital stay were 4.2 (2.6, 7.3), 4.0 (2.3, 6.8), and 4.3 (2.5, 7.4) days for SARS-CoV-2, influenza, and RSV episodes resulting in admission. These estimates provide a basis for modeling real-world clinical care requirements and the progression of respiratory viral infections.

Indexed as

COVID-19Influenza, HumanRespiratory Syncytial Virus InfectionsAdolescentAdultAgedAged, 80 and overChildChild, PreschoolComputational BiologyDelivery of Health Care, IntegratedDisease ProgressionFemaleHospitalizationHumansInfant

Identifiers

PMID41259406
PMCPMC12643285

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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.