tetano
Editor, Senior Moderator
Nat Commun
. 2025 May 14;16(1):4466.
doi: 10.1038/s41467-025-57370-z. Identification of undetected SARS-CoV-2 infections by clustering of Nucleocapsid antibody trajectories
Leslie R Zwerwer[SUP] 1 2 3 [/SUP], Tim E A Peto[SUP] #[/SUP][SUP] 4 5 6 7 [/SUP], Koen B Pouwels[SUP] #[/SUP][SUP] 6 8 9 [/SUP], Ann Sarah Walker[SUP] #[/SUP][SUP] 4 6 7 [/SUP]; COVID-19 Infection Survey team
Collaborators, Affiliations
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.
. 2025 May 14;16(1):4466.
doi: 10.1038/s41467-025-57370-z. Identification of undetected SARS-CoV-2 infections by clustering of Nucleocapsid antibody trajectories
Leslie R Zwerwer[SUP] 1 2 3 [/SUP], Tim E A Peto[SUP] #[/SUP][SUP] 4 5 6 7 [/SUP], Koen B Pouwels[SUP] #[/SUP][SUP] 6 8 9 [/SUP], Ann Sarah Walker[SUP] #[/SUP][SUP] 4 6 7 [/SUP]; COVID-19 Infection Survey team
Collaborators, Affiliations
- PMID: 40368905
- DOI: 10.1038/s41467-025-57370-z
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.