Quantifying SARS-CoV-2 Omicron variant spread and the impact of non-pharmaceutical interventions in Newfoundland and Labrador, Canada.
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Where this comes from
- Record sourced from PubMed, PMID 41950273.
- Also identified by DOI 10.1371/journal.pcbi.1013562 and PMC identifier 13089892.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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, [Formula: see text] = 1.98, 95% CI: 1.58-2.37), higher when open (mean [Formula: see text] = 2.71, 95% CI: 2.31-3.11), and of the alert levels, the strictest alert level reduced transmission the most (mean [Formula: see text] = 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.
Medical subject headings
- COVID-19
- SARS-CoV-2