Evidence map›Paper›PMID 38166805›Full record

ArticleBMC public health2024

A response playbook for early detection and population surveillance of new SARS-CoV-2 variants in a regional public health laboratory.

Hannah J Barbian, Alyse Kittner, Richard Teran, Sofiya Bobrovska, Xueting Qiu, Kayla English, Stefan J Green, Isaac Ghinai, Massimo Pacilli, Mary K Hayden

Open access · goldAbstract read
In one paragraph

Article in BMC public health, 2024. 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
0.8field-weighted citation impact, top 33% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

10 authors at 3 institutions in 1 country.

Hannah J BarbianDepartment of Internal Medicine, Regional Innovative Public Health Laboratory, Rush University Medical Center, Jelke 1259, 1750 W Harrison St, Chicago, IL, 60612, USA. hannah_j_barbian@rush.edu.
Alyse KittnerDepartment of Internal Medicine, Regional Innovative Public Health Laboratory, Rush University Medical Center, Jelke 1259, 1750 W Harrison St, Chicago, IL, 60612, USA.
Richard TeranChicago Department of Public Health, Chicago, IL, USA.
Sofiya BobrovskaDepartment of Internal Medicine, Regional Innovative Public Health Laboratory, Rush University Medical Center, Jelke 1259, 1750 W Harrison St, Chicago, IL, 60612, USA.
Xueting QiuCenter for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.
Kayla EnglishChicago Department of Public Health, Chicago, IL, USA.
Stefan J GreenDepartment of Internal Medicine, Regional Innovative Public Health Laboratory, Rush University Medical Center, Jelke 1259, 1750 W Harrison St, Chicago, IL, 60612, USA.
Isaac GhinaiChicago Department of Public Health, Chicago, IL, USA.
Massimo PacilliChicago Department of Public Health, Chicago, IL, USA.
Mary K HaydenDepartment of Internal Medicine, Regional Innovative Public Health Laboratory, Rush University Medical Center, Jelke 1259, 1750 W Harrison St, Chicago, IL, 60612, USA.
Rush University Medical Center · USChicago Department of Public Health · USCenter for Disease Dynamics, Economics & Policy · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTimely genomic surveillance is required to inform public health responses to new SARS-CoV-2 variants. However, the processes involved in local genomic surveillance introduce inherent time constraints. The Regional Innovative Public Health Laboratory in Chicago developed and employed a genomic surveillance response playbook for the early detection and surveillance of emerging SARS-CoV-2 variants.

methodsThe playbook outlines modifications to sampling strategies, laboratory workflows, and communication processes based on the emerging variant's predicted viral characteristics, observed public health impact in other jurisdictions and local community risk level. The playbook outlines procedures for implementing and reporting enhanced and accelerated genomic surveillance, including supplementing whole genome sequencing (WGS) with variant screening by quantitative PCR (qPCR).

resultsThe ability of the playbook to improve the response to an emerging variant was tested for SARS-CoV-2 Omicron BA.1. Increased submission of clinical remnant samples from local hospital laboratories enabled detection of a new variant at an average of 1.4% prevalence with 95% confidence rather than 3.5% at baseline. Genotyping qPCR concurred with WGS lineage assignments in 99.9% of 1541 samples with results by both methods, and was more sensitive, providing lineage results in 90.4% of 1833 samples rather than 85.1% for WGS, while significantly reducing the time to lineage result.

conclusionsThe genomic surveillance response playbook provides a structured, stepwise, and data-driven approach to responding to emerging SARS-CoV-2 variants. These pre-defined processes can serve as a template for other genomic surveillance programs to streamline workflows and expedite the detection and public health response to emerging variants. Based on the processes piloted during the Omicron BA.1 response, this method has been applied to subsequent Omicron subvariants and can be readily applied to future SARS-CoV-2 emerging variants and other public health surveillance activities.

Indexed as

COVID-19Laboratories, HospitalHumansPublic HealthPublic Health SurveillanceSARS-CoV-2Disease outbreaksGenomic surveillanceInfectious diseasePublic health preparednessPublic health systemsSARS-CoV-2Surveillance

Identifiers

PMID38166805
PMCPMC10763119
OpenAlexW4390509695

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.