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

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.

Fig. 1. N-antibody trajectories for the different N-antibody and swab-positive infection groups (restricted to those with ≥4 N-antibody measurements, see Supplementary Fig. 2).

For comparability, trajectories are centred on the midpoint between the maximum difference between any two consecutive measurements per participant. This approximates the hypothetical infection date for those with an N-antibody trajectory compatible with infection, but can create a small but arbitrary distortion in those without swab-positive infections and classified as flat or decreasing. Each frame contains a random sample of 200 N-antibody trajectories (see Fig. 2 for numbers and cell percentages). Black line depicts a generalised additive modelling smooth for all N-antibody measurements assayed between the 10^th and 90^th percentile of the centred days in each cluster.

Fig. 2. Number of participants classified in each N-antibody trajectory-based and swab-positive infection group.

Colour intensity of the tiles indicates the row-percentage. Of all 13,324 participants with a swab-positive infection (i) before or (ii) before and during their study period, only 108 (0.8%) participants had two distinct swab-positive infections before their study period and of all 25,052 participants with a swab-positive infection (i) during or (ii) before and during their study period, 350 (1.4%) participants had two or more swab-positive infections during their study period. Note: showing raw counts, total percentages, row and column percentages.

Fig. 3. Comparison of the N-antibody trajectory-based classification, fixed 30 ng/mL classification, fourfold-based classification and fourfold-based sensitivity analysis across different data sources used to define swab-positive infections.

a Percentage of participants with N-antibody (hypothetical) infections that were identified using swab-positivity (b) Percentage of participants without N-antibody (hypothetical) infections with no positive swab. In Fig. 3b the four lines (nearly) overlap. Survey: only using positive and negative swab PCR test results from the COVID-19 Infection Survey to define swab-positive infections; NTP: using positive PCR and LFT swab test results from national testing programmes in England and Wales; Self: using self-reported positive swab test results; Think: using self-report on thinking one had had COVID-19.