Wastewater Surveillance to Confirm Differences in Influenza A Infection between Michigan, USA, and Ontario, Canada, September 2022–March 2023
Bibliographic record
Abstract
RESEARCHT he SARS-CoV-2 pandemic reasserted the impor- tance of epidemic preparedness and surveillance systems for infectious diseases (1).Informed responses to public health challenges require that data be available to decision-makers in a timely manner for early interventions (1).However, traditional clinical based measures of disease incidence have limited use in providing early warnings.Relying on influenzalike illness data is problematic because of difficulty distinguishing between infections ascribed to influenza A virus (IAV), influenza B virus, SARS-CoV-2, or respiratory syncytial virus (2).Virologic surveillance enables respiratory illness to be classified on the basis of etiologic agent.However, results are often slow, and interpretation must account for factors such as test-seeking behavior, accessibility of healthcare services, severity of infection, diagnostic practices of healthcare providers, and hospital protocols.In addition, laboratory capacity may be exceeded, and testing is expensive (3,4).Wastewater surveillance (WS) is shown to be a practical approach for disease surveillance at various spatial scales, offering effectiveness and economic advantage (5,6).WS for SARS-CoV-2 relies on quantifying viral RNA shed in feces and has substantially increased in use since its implementation to track infections during the COVID-19 pandemic.Studies have found the concentration of SARS-CoV-2 RNA in municipal sewage covaries with the levels of disease circulating within the community served and can predict trends in clinical cases and hospitalizations (7,8).In addition, WS has the potential to be rapid; sample Wastewater Surveillance to Confirm Differences in Influenza AInfection between Michigan, USA, and Ontario, Canada,
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 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".