Evidence map›Paper›PMID 38312225›Full record

ArticlePNAS nexus2024

Age-specific transmission dynamics of SARS-CoV-2 during the first 2 years of the pandemic.

Otilia Boldea, Amir Alipoor, Sen Pei, Jeffrey Shaman, Ganna Rozhnova

Abstract read
In one paragraph

Article in PNAS nexus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. medRxiv : the preprint server for health sciences · 2026
    Article
  3. Reconstructing the early spatial spread of pandemic respiratory viruses in the United States.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. 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

5 authors.

Otilia BoldeaDepartment of Econometrics and OR, Tilburg School of Economics and Management, Tilburg University, Tilburg 5037 AB, The Netherlands.ORCID https://orcid.org/0000-0002-6135-430X
Amir AlipoorDepartment of Econometrics and OR, Tilburg School of Economics and Management, Tilburg University, Tilburg 5037 AB, The Netherlands.ORCID https://orcid.org/0000-0002-0443-4995
Sen PeiDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.ORCID https://orcid.org/0000-0002-7072-2995
Jeffrey ShamanDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.ORCID https://orcid.org/0000-0002-7216-7809
Ganna RozhnovaJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht 3584 CX, The Netherlands.ORCID https://orcid.org/0000-0002-6725-7359

Funding

Quantifying Error Growth to Improve Infectious Disease Forecast AccuracyR01AI163023 · NIAID · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI JEFFREY L SHAMAN · 2021 to 2026
$3.3M
NIAID NIH HHS R01 AI163023
6 · The paper itself

Abstract

During its first 2 years, the SARS-CoV-2 pandemic manifested as multiple waves shaped by complex interactions between variants of concern, non-pharmaceutical interventions, and the immunological landscape of the population. Understanding how the age-specific epidemiology of SARS-CoV-2 has evolved throughout the pandemic is crucial for informing policy decisions. In this article, we aimed to develop an inference-based modeling approach to reconstruct the burden of true infections and hospital admissions in children, adolescents, and adults over the seven waves of four variants (wild-type, Alpha, Delta, and Omicron BA.1) during the first 2 years of the pandemic, using the Netherlands as the motivating example. We find that reported cases are a considerable underestimate and a generally poor predictor of true infection burden, especially because case reporting differs by age. The contribution of children and adolescents to total infection and hospitalization burden increased with successive variants and was largest during the Omicron BA.1 period. However, the ratio of hospitalizations to infections decreased with each subsequent variant in all age categories. Before the Delta period, almost all infections were primary infections occurring in naive individuals. During the Delta and Omicron BA.1 periods, primary infections were common in children but relatively rare in adults who experienced either reinfections or breakthrough infections. Our approach can be used to understand age-specific epidemiology through successive waves in other countries where random community surveys uncovering true SARS-CoV-2 dynamics are absent but basic surveillance and statistics data are available.

Indexed as

age-specific transmission dynamicsCOVID-19 and SARS-CoV-2infection and hospitalization burdensinferencemathematical modelrespiratory viruses

Identifiers

PMID38312225
PMCPMC10837015

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.