Utility of the Peterborough Public Health COVID-19 rapid antigen test self-report tool: Implications for COVID-19 surveillance
Bibliographic record
Abstract
Background: The ongoing coronavirus disease 2019 (COVID-19) pandemic has necessitated novel testing strategies, including the use of rapid antigen tests (RATs).The widespread distribution of RATs to the public prompted Peterborough Public Health to launch a pilot RAT self-report tool to assess its utility in COVID-19 surveillance.The objective of this study is to investigate the utility of RAT using correlations between RAT self-report results and other indicators of COVID-19. Methods:We investigated the association between RAT results, PCR test results and wastewater levels of nmN1N2 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genes (to infer COVID-19 levels) using Pearson's correlation coefficient.Percent positivity and count of positive tests for RATs and polymerase chain reaction (PCR) tests were analyzed. Results:The PCR percent positivity and wastewater were weakly correlated (r=0.33,p=0.022), as were RAT percent positivity and wastewater nmN1N2 levels (r=0.33,p=0.002).The RAT percent positivity and PCR percent positivity were not significantly correlated (r=-0.035,p=0.75).Count of positive RATs and count of positive PCR tests were moderately correlated (r=0.59,p<0.001).Wastewater nmN1N2 levels were not significantly correlated with either count of positive RATs (r=0.019,p=0.864) or count of positive PCR tests (r=0.004,p=0.971).Conclusion: Our results support the use of RAT self-reporting as a low-cost simple adjunctive COVID-19 surveillance tool, and suggest that its utility is greatest when considering an absolute count of positive RATs rather than percent positivity due to reporting bias towards positive tests.These results can help inform COVID-19 surveillance strategies of local public health units and encourage the use of a RAT self-report tool.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".