Quantifying SARS-CoV-2 Omicron variant spread and the impact of non-pharmaceutical interventions in Newfoundland and Labrador, Canada
Bibliographic record
Abstract
Abstract The highly transmissible Omicron variant of SARS-CoV-2 caused many infections in Newfoundland and Labrador, Canada, and the fraction of infections that were unreported varied as PCR testing capacity was exceeded and eligibility rules changed. Due to these inconsistencies in the testing rate, we developed a mechanistic model that was calibrated to serological data (Dec 2021–May 2022) to estimate underreporting and understand the impact of non-pharmaceutical interventions on transmission. Our model considers the epidemiology of SARS-CoV-2 spread, natural and vaccine-derived immunity, and the booster dose vaccination campaign that was ongoing in Newfoundland and Labrador. We found that during the early spread of the Omicron variant, when the eligibility for tests that were reported in the provincial counts was less restrictive, three or fewer infections were unreported per reported case. After March 17, 2022, when test eligibility was more restrictive, the underreporting rate increased steadily, with an average of 24.2 infections unreported infections per reported case. We found that Omicron transmission was lower when schools were closed (mean control reproduction number, ℛ c = 1.98, 95% CI: 1.58–2.37), higher when open (mean ℛ c = 2.71, 95% CI: 2.31–3.11), and of the alert levels, the strictest alert level reduced transmission the most (mean ℛ c = 2.23, 95% CI: 1.98–2.53). When underreporting rates vary, the impact of non-pharmaceutical interventions, such as alert level systems and school closures, cannot be determined from reported cases. Our findings highlight the value of combining serological data with modelling to determine the impact of non-pharmaceutical interventions during pandemics when surveillance systems are constrained. Author summary Reported COVID-19 cases often underestimate the number of infections, especially when testing capacity is exceeded or eligibility rules change. This occurred in Newfoundland and Labrador, Canada, during the spread of the Omicron variant, when testing rates were uneven and many infections were unreported. Our analysis shows that underreporting increased substantially when eligibility for tests that could be reported in the official counts was restricted to high-risk individuals and people who work with high-risk individuals. Given this eligibility change, the underreporting ratio increased from three or less to an average of 24.2 unreported infections per reported case. We used an epidemiological model to account for the many factors, such as asymptomatic infections and vaccination status, that are known to affect infection spread. We found that transmission was lower when schools were closed, higher when schools were open, and of the alert levels, the strictest alert level reduced spread the most. These results highlight that reported cases alone can produce inaccurate results when testing systems are constrained and the rate of testing is variable. Using our approach of combining serological data with modelling enabled us to evaluate the impact of public health measures. These results support knowledge mobilization to explain to the public why particular public health measures are being implemented during an emergency.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".