Multiplex assays for the identification of serological signatures of SARS-CoV-2 infection: an antibody-based diagnostic and machine learning study
The Lancet Microbe·
- DOI
- 10.1016/s2666-5247(20)30197-x
- PMID
- 33521709
- PMCID
- PMC7837364
- OpenAlex
- W3116955314
- Study type
- Mathematical modelling
- Publisher
- Elsevier BV
- Article type
- journal-article
- Integrity
- current
Publication version
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Open linked preprint →Why this research matters now
Multiplex serological approaches offer potential solutions for classifying individuals infected more than six months prior and for measuring seroprevalence in very low-transmission settings, addressing key challenges in SARS-CoV-2 serological surveillance.
Structured evidence summary
Research question
The study aimed to develop more accurate serological diagnostics for SARS-CoV-2 infection by measuring antibody responses to multiple antigens and using machine learning to classify individuals with previous infection.
Study design
A multiplex serological assay measured IgG and IgM antibodies to multiple SARS-CoV-2 and other coronavirus antigens in serum samples from RT-PCR-confirmed cases and pre-pandemic controls. Machine learning classifiers were trained on the multiplex data, and a Bayesian mathematical model informed by prior coronavirus data estimated time-varying antibody responses and diagnostic performance over one year post-infection.
Population and setting
Serum samples were collected from 215 adults in four French hospitals, including 53 patients and 162 healthcare workers with RT-PCR-confirmed SARS-CoV-2 infection up to 39 days after symptom onset. Negative control samples came from 335 healthy adult blood donors in France, Thailand, and Peru collected before the pandemic.
Main findings
IgG antibodies to trimeric spike protein identified previous infection with 91.6% sensitivity and 99.1% specificity. A multiplex serological signature using IgG and IgM to multiple antigens achieved 98.8% sensitivity and 99.3% specificity. Model estimates suggest that one year post-infection, a four-antigen multiplex assay can maintain 96.4% sensitivity compared to 88.7% for a monoplex assay, and multiplex statistical analysis enables seroprevalence estimation below 2% in low-transmission settings.
Public-health relevance
Multiplex serological approaches offer potential solutions for classifying individuals infected more than six months prior and for measuring seroprevalence in very low-transmission settings, addressing key challenges in SARS-CoV-2 serological surveillance.
Important limitations
This summary is limited to the supplied single-article abstract and metadata and requires the original paper for decision-grade interpretation of methods, validation cohorts, generalizability, and implementation considerations.
GIDS interpretation
The article was classified under COVID-19 and SARS diseases with topical links to diagnostics, surveillance, and transmission dynamics, reflecting its focus on serological tools for infection detection and population monitoring across multiple countries.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 10 auditable classifier relationships to diseases, places, topics, and study design.