The impact of seasonal respiratory virus transmission on syndromic surveillance for COVID-19 in Ontario, Canada
Bibliographic record
Abstract
ABSTRACT Background Syndromic surveillance systems for COVID-19 are being increasingly used to track and predict outbreaks of confirmed cases. Seasonal circulating respiratory viruses share syndromic overlap with COVID-19, and it is unknown how they will impact the performance of syndromic surveillance tools. Here we investigated the role of non-SARS-CoV-2 respiratory virus test positivity on COVID-19 two independent syndromic surveillance systems in Ontario, Canada. Methods We compared the weekly number of reported COVID-19 cases reported in the province of Ontario against two syndromic surveillance metrics: 1) the proportion of respondents with a self-reported COVID-like illness (CLI) from COVID Near You (CNY) and 2) the proportion of emergency department visits for upper respiratory conditions from the Acute Care Enhanced Surveillance (ACES) system. Separately, we plotted the percent positivity for other seasonal respiratory viruses over the same time period and reported Pearson’s correlation coefficients before and after the uncoupling of syndromic tools to COVID-19 cases. Results There were strong positive correlations of both CLI and ED visits for upper respiratory causes with COVID-19 cases up to and including a rise in entero/rhinovirus (r = 0.86 and 0.87, respectively). There was a strong negative correlation of both CLI and ED visits for upper respiratory causes with COVID-19 cases (r = −0.85 and −0.91, respectively) during a fall in entero/rhinovirus. Interpretation Two methods of syndromic surveillance showed strong positive correlations with COVID-19 confirmed case counts before and during a rise in circulating entero/rhinovirus. However, as positivity for enterovirus/rhinovirus fell in late September 2020, syndromic signals became uncoupled from COVID-19 cases and instead tracked the fall in entero/rhinovirus. This finding provides proof-of-principle that regional transmission of seasonal respiratory viruses may complicate the interpretation of COVID-19 surveillance data. It is imperative that surveillance systems incorporate other respiratory virus testing data in order to more accurately track and forecast COVID-19 disease activity.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".