Evidence map›Paper›PMID 36624344›Full record

ArticleNature genetics2023

SARS-CoV-2 diagnostic testing rates determine the sensitivity of genomic surveillance programs.

Alvin X Han, Amy Toporowski, Jilian A Sacks, Mark D Perkins, Sylvie Briand, Maria van Kerkhove, Emma Hannay, Sergio Carmona, Bill Rodriguez, Edyth Parker and 2 more

Open access · hybridAbstract read
In one paragraph

Article in Nature genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
6.2field-weighted citation impact, top 3% of its field
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

21 citing papers in PubMed, 33 citations in OpenAlex.

  1. Article
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  9. Toward optimal disease surveillance with graph-based active learning.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  10. Article
  11. Review
  12. Article
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  16. Surveillance for variants of SARS-CoV-2 to inform risk assessments.Bulletin of the World Health Organization · 2023
    Article
  17. Development of practical techniques for simultaneous detection and distinction of current and emerging SARS-CoV-2 variants.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2023
    Review
  18. Article
  19. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 5 institutions in 3 countries.

Alvin X HanDepartment of Medical Microbiology and Infection Prevention, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands. x.han@amsterdamumc.nl.ORCID 0000-0001-6281-8498
Amy ToporowskiFoundation for Innovative New Diagnostics (FIND), Geneva, Switzerland.
Jilian A SacksDepartment of Epidemic and Pandemic Preparedness and Prevention, Emergency Preparedness Programme, World Health Organization, Geneva, Switzerland.
Mark D PerkinsDepartment of Epidemic and Pandemic Preparedness and Prevention, Emergency Preparedness Programme, World Health Organization, Geneva, Switzerland.
Sylvie BriandDepartment of Epidemic and Pandemic Preparedness and Prevention, Emergency Preparedness Programme, World Health Organization, Geneva, Switzerland.
Maria van KerkhoveDepartment of Epidemic and Pandemic Preparedness and Prevention, Emergency Preparedness Programme, World Health Organization, Geneva, Switzerland.
Emma HannayFoundation for Innovative New Diagnostics (FIND), Geneva, Switzerland.
Sergio CarmonaFoundation for Innovative New Diagnostics (FIND), Geneva, Switzerland.
Bill RodriguezFoundation for Innovative New Diagnostics (FIND), Geneva, Switzerland.
Edyth ParkerDepartment of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA.
Brooke E Nichols *Department of Medical Microbiology and Infection Prevention, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands.
Colin A Russell *Department of Medical Microbiology and Infection Prevention, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands. c.a.russell@amsterdamumc.nl.ORCID 0000-0002-2113-162X
FIND · CHWorld Health Organization · CHBoston University · USAmsterdam University Medical Centers · NLScripps Research Institute · US

Funding

Forecasting influenza epidemics using a mechanistic epidemic modelR01AI132362 · NIAID · KAISER FOUNDATION RESEARCH INSTITUTE · PI MANGIONE-SMITH, RITA · 2017 to 2020
$1.0M
NIAID NIH HHS R01 AI132362World Health Organization 001
6 · The paper itself

Abstract

The first step in SARS-CoV-2 genomic surveillance is testing to identify people who are infected. However, global testing rates are falling as we emerge from the acute health emergency and remain low in many low- and middle-income countries (mean = 27 tests per 100,000 people per day). We simulated COVID-19 epidemics in a prototypical low- and middle-income country to investigate how testing rates, sampling strategies and sequencing proportions jointly impact surveillance outcomes, and showed that low testing rates and spatiotemporal biases delay time to detection of new variants by weeks to months and can lead to unreliable estimates of variant prevalence, even when the proportion of samples sequenced is increased. Accordingly, investments in wider access to diagnostics to support testing rates of approximately 100 tests per 100,000 people per day could enable more timely detection of new variants and reliable estimates of variant prevalence. The performance of global SARS-CoV-2 genomic surveillance programs is fundamentally limited by access to diagnostic testing.

Indexed as

COVID-19EpidemicsCOVID-19 TestingDiagnostic Techniques and ProceduresGenomicsHumansSARS-CoV-2

Identifiers

PMID36624344
PMCPMC9839449
OpenAlexW4313893189

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.