Identification of undetected SARS-CoV-2 infections by clustering of Nucleocapsid antibody trajectories

During the COVID-19 pandemic, numerous SARS-CoV-2 infections remained undetected. We combined results from routine monthly nose and throat swabs, and self-reported positive swab tests, from a UK household survey, linked to national swab testing programme data from England and Wales, together with Nucleocapsid (N-)antibody trajectories clustered using a longitudinal variation of K-means (N = 185,646) to estimate the number of infections undetected by either approach. Using N-antibody (hypothetical) infections and swab-positivity, we estimated that 7.4% (95%CI: 7.0-7.8%) of all true infections (detected and undetected) were undetected by both approaches, 25.8% (25.5-26.1%) by swab-positivity-only and 28.6% (28.4-28.9%) by trajectory-based N-antibody-classifications-only. Congruence with swab-positivity was respectively much poorer and slightly better with N-antibody classifications based on fixed thresholds or fourfold increases. Using multivariable logistic regression N-antibody seroconversion was more likely as age increased between 30-60 years, in non-white participants, those less (recently/frequently) vaccinated, for lower cycle threshold values in the range above 30, and in symptomatic and Delta (vs. BA.1) infections. Comparing swab-positivity data sources showed that routine monthly swabs were insufficient to detect infections and incorporating national testing programme/self-reported data substantially increased detection. Overall, whilst N-antibody serosurveillance can identify infections undetected by swab-positivity, optimal use requires fourfold-increase-based or trajectory-based analysis.

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Publication Details

Journal
Nature Communications
Published
2025-05-14
DOI
https://doi.org/10.1038/s41467-025-57370-z
Citations
1
Primary Topic
SARS-CoV-2 and COVID-19 Research
Type
article
Field-Weighted Citation Impact
1.40

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article

Identification of undetected SARS-CoV-2 infections by clustering of Nucleocapsid antibody trajectories

Leslie R. Zwerwer, Tim Peto, Koen B. Pouwels, Ann Sarah Walker
1 citations
Nature Communications
SARS-CoV-2 and COVID-19 Research
1.40
article

Identification of undetected SARS-CoV-2 infections by clustering of Nucleocapsid antibody trajectories

Leslie R. Zwerwer, Tim Peto, Koen B. Pouwels, Ann Sarah Walker
article en
1 citations

Abstract

During the COVID-19 pandemic, numerous SARS-CoV-2 infections remained undetected. We combined results from routine monthly nose and throat swabs, and self-reported positive swab tests, from a UK household survey, linked to national swab testing programme data from England and Wales, together with Nucleocapsid (N-)antibody trajectories clustered using a longitudinal variation of K-means (N = 185,646) to estimate the number of infections undetected by either approach. Using N-antibody (hypothetical) infections and swab-positivity, we estimated that 7.4% (95%CI: 7.0-7.8%) of all true infections (detected and undetected) were undetected by both approaches, 25.8% (25.5-26.1%) by swab-positivity-only and 28.6% (28.4-28.9%) by trajectory-based N-antibody-classifications-only. Congruence with swab-positivity was respectively much poorer and slightly better with N-antibody classifications based on fixed thresholds or fourfold increases. Using multivariable logistic regression N-antibody seroconversion was more likely as age increased between 30-60 years, in non-white participants, those less (recently/frequently) vaccinated, for lower cycle threshold values in the range above 30, and in symptomatic and Delta (vs. BA.1) infections. Comparing swab-positivity data sources showed that routine monthly swabs were insufficient to detect infections and incorporating national testing programme/self-reported data substantially increased detection. Overall, whilst N-antibody serosurveillance can identify infections undetected by swab-positivity, optimal use requires fourfold-increase-based or trajectory-based analysis.

Nature CommunicationsVol. 16(1)
Department of Health and Social Care (GB), Office for National Statistics (GB), University Medical Center Groningen (NL), University of Groningen (NL), The Francis Crick Institute (GB), Scottish Government (GB), Welsh Government (GB), John Radcliffe Hospital (GB), Open Data Institute (GB), University of Manchester (GB), IQVIA (United Kingdom) (GB), National Institute for Health and Care Research (GB), University of Oxford (GB), Public Health Scotland (GB), Oxford University Hospitals NHS Trust (GB), UK Health Security Agency (GB), University College London (GB)
National Institute for Health and Care Research, Department of Health and Social Care
No poverty
Openalex Percentile: Top 21%
SARS-CoV-2 and COVID-19 Research
1.40
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