Evidence map›Paper›PMID 34083451›Full record

ArticleScience (New York, N.Y.)2021

Estimating epidemiologic dynamics from cross-sectional viral load distributions.

James A Hay, Lee Kennedy-Shaffer, Sanjat Kanjilal, Niall J Lennon, Stacey B Gabriel, Marc Lipsitch, Michael J Mina

Abstract read
In one paragraph

Article in Science (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 150 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
150citing papers in PubMed, 1 pooled it
–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

150 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. medRxiv : the preprint server for health sciences · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Article

90 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

James A Hay *Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. jhay@hsph.harvard.edu lkennedyshaffer@vassar.edu mmina@hsph.harvard.edu.ORCID 0000-0002-1998-1844
Lee Kennedy-Shaffer *Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. jhay@hsph.harvard.edu lkennedyshaffer@vassar.edu mmina@hsph.harvard.edu.ORCID 0000-0001-7604-3638
Sanjat KanjilalDepartment of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA, USA.ORCID 0000-0002-1221-5725
Niall J LennonBroad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0000-0002-2874-7371
Stacey B GabrielBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Marc LipsitchCenter for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.ORCID 0000-0003-1504-9213
Michael J MinaCenter for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. jhay@hsph.harvard.edu lkennedyshaffer@vassar.edu mmina@hsph.harvard.edu.ORCID 0000-0002-0674-5762

Funding

Understanding and controlling antibiotic resistanceU54GM088558 · NIGMS · HARVARD SCHOOL OF PUBLIC HEALTH · PI HANAGE, WILLIAM · 2009 to 2018
$26.4M
Delphi Influenza Forecasting Center of ExcellenceU01IP001121 · IP · CARNEGIE-MELLON UNIVERSITY · PI ROSENFELD, RONALD · 2019 to 2023
$4.8M
Casual, Statistical and Mathematical Modeling with Serologic DataU01CA261277 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI HANAGE, WILLIAM, LIPSITCH, MARC · 2020 to 2023
$2.8M
Deconvolution and reconstruction of immune histories to enhance infectious disease prevention and vaccination strategies and optimize surveillance effortsDP5OD028145 · OD · HARVARD SCHOOL OF PUBLIC HEALTH · PI HOFMAN, ALBERT · 2019 to 2021
$1.2M
ACL HHS U01IP001121NCI NIH HHS U01 CA261277NCIRD CDC HHS U01 IP001121NIGMS NIH HHS U54 GM088558NIH HHS DP5 OD028145
6 · The paper itself

Abstract

Estimating an epidemic's trajectory is crucial for developing public health responses to infectious diseases, but case data used for such estimation are confounded by variable testing practices. We show that the population distribution of viral loads observed under random or symptom-based surveillance-in the form of cycle threshold (Ct) values obtained from reverse transcription quantitative polymerase chain reaction testing-changes during an epidemic. Thus, Ct values from even limited numbers of random samples can provide improved estimates of an epidemic's trajectory. Combining data from multiple such samples improves the precision and robustness of this estimation. We apply our methods to Ct values from surveillance conducted during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in a variety of settings and offer alternative approaches for real-time estimates of epidemic trajectories for outbreak management and response.

Indexed as

Viral LoadCOVID-19COVID-19 Nucleic Acid TestingCross-Sectional StudiesEpidemiological MonitoringHumansIncidenceModels, TheoreticalPandemicsSARS-CoV-2

Identifiers

PMID34083451
PMCPMC8527857

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.