Enhancing detection of SARS-CoV-2 re-infections using longitudinal sero-monitoring: demonstration of a methodology in a cohort of people experiencing homelessness in Toronto, Canada
Bibliographic record
Abstract
BACKGROUND: Accurate estimation of SARS-CoV-2 re-infection is crucial to understanding the connection between infection burden and adverse outcomes. However, relying solely on PCR testing results in underreporting. We present a novel approach that includes longitudinal serologic data, and compared it against testing alone among people experiencing homelessness. METHODS: We recruited 736 individuals experiencing homelessness in Toronto, Canada, between June and September 2021. Participants completed surveys and provided saliva and blood serology samples every three months over 12 months of follow-up. Re-infections were defined as: positive PCR or rapid antigen test (RAT) results > 90 days after initial infection; new serologic evidence of infection among individuals with previous infection who sero-reverted; or increases in anti-nucleocapsid in seropositive individuals whose levels had begun to decrease. RESULTS: Among 381 participants at risk, we detected 37 re-infections through PCR/RAT and 98 re-infections through longitudinal serology. The comprehensive method identified 37.4 re-infection events per 100 person-years, more than four-fold more than the rate detected through PCR/RAT alone (9.0 events/100 person-years). Almost all test-confirmed re-infections (85%) were also detectable by longitudinal serology. CONCLUSIONS: Longitudinal serology significantly enhances the detection of SARS-CoV-2 re-infections. Our findings underscore the importance and value of combining data sources for effective research and public health surveillance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".