Evidence map›Paper›PMID 35664998›Full record

ArticlemedRxiv : the preprint server for health sciences2022

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 · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed, 9 citations in OpenAlex.

  1. Article
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 & Infection Prevention, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
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 NicholsDepartment of Medical Microbiology & Infection Prevention, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
Colin A RussellDepartment of Medical Microbiology & Infection Prevention, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
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 AI132362
6 · The paper itself

Abstract

The first step in SARS-CoV-2 genomic surveillance is testing to identify infected people. However, global testing rates are falling as we emerge from the acute health emergency and remain low in many low- and middle-income countries (LMICs) (mean = 27 tests/100,000 people/day). We simulated COVID-19 epidemics in a prototypical LMIC 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 ~100 tests/100,000 people/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.

Identifiers

PMID35664998
PMCPMC9164450
OpenAlexW4281390722

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.