Evidence map›Paper›PMID 32953275›Full record

ArticlePeerJ2020

Collaborative networks enable the rapid establishment of serological assays for SARS-CoV-2 during nationwide lockdown in New Zealand.

Reuben McGregor, Alana L Whitcombe, Campbell R Sheen, James M Dickson, Catherine L Day, Lauren H Carlton, Prachi Sharma, J Shaun Lott, Barbara Koch, Julie Bennett and 8 more

Open access · goldAbstract read
In one paragraph

Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.3field-weighted citation impact, top 28% 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

6 citing papers in PubMed, 12 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Observational
  6. 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

18 authors at 5 institutions in 1 country.

Reuben McGregor *Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
Alana L Whitcombe *Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
Campbell R SheenProtein Science and Engineering, Callaghan Innovation, Christchurch, New Zealand.
James M DicksonSchool of Biological Sciences, University of Auckland, Auckland, New Zealand.
Catherine L DayDepartment of Biochemistry, University of Otago, Dunedin, New Zealand.
Lauren H CarltonFaculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
Prachi SharmaFaculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.ORCID 0000-0002-6932-8472
J Shaun LottMaurice Wilkins Centre, University of Auckland, Auckland, New Zealand.ORCID 0000-0003-3660-452X
Barbara KochProtein Science and Engineering, Callaghan Innovation, Christchurch, New Zealand.
Julie BennettDepartment of Public Health, University of Otago, Wellington, New Zealand.
Michael G BakerDepartment of Public Health, University of Otago, Wellington, New Zealand.ORCID 0000-0002-1865-1536
Stephen R RitchieFaculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
Shivani Fox-LewisDepartment of Microbiology, LabPLUS, Auckland City Hospital, Auckland, New Zealand.
Susan C MorpethMiddlemore Hospital, Auckland, New Zealand.
Susan L TaylorMiddlemore Hospital, Auckland, New Zealand.
Sally A RobertsMaurice Wilkins Centre, University of Auckland, Auckland, New Zealand.
Rachel H WebbFaculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
Nicole J MorelandFaculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.ORCID 0000-0001-6548-637X
University of Auckland · NZUniversity of Otago · NZCallaghan Innovation · NZMiddlemore Hospital · NZAuckland City Hospital · NZ

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSerological assays that detect antibodies to SARS-CoV-2 are critical for determining past infection and investigating immune responses in the COVID-19 pandemic. We established ELISA-based immunoassays using locally produced antigens when New Zealand went into a nationwide lockdown and the supply chain of diagnostic reagents was a widely held domestic concern. The relationship between serum antibody binding measured by ELISA and neutralising capacity was investigated using a surrogate viral neutralisation test (sVNT).

methodsA pre-pandemic sera panel (

resultsThe calculated cut-off (>0.2) in the two-step ELISA maximised specificity by classifying all pre-pandemic samples as negative. Sera from all PCR-confirmed COVID-19 patients were classified as seropositive by ELISA ≥7 days after symptom onset. There was 100% concordance between the two-step ELISA and the sVNT with all 7+ day sera from PCR‑confirmed COVID-19 patients also classified as positive with respect to neutralising antibodies. Of the symptomatic PCR-negative cohort, one individual with notable travel history was classified as positive by two-step ELISA and sVNT, demonstrating the value of serology in detecting prior infection.

conclusionsThese serological assays were established and assessed at a time when human activity was severely restricted in New Zealand. This was achieved by generous sharing of reagents and technical expertise by the international scientific community, and highly collaborative efforts of scientists and clinicians across the country. The assays have immediate utility in supporting clinical diagnostics, understanding transmission in high-risk cohorts and underpinning longer‑term 'exit' strategies based on effective vaccines and therapeutics.

Indexed as

COVID-19Neutralising antibodiesSARS-CoV-2SerologySpike protein

Identifiers

PMID32953275
PMCPMC7474877
OpenAlexW3082194771

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.