PS01 MEPI-20

Quantifying the Hidden Burden of Omicron and the Impact of Alert Level System in Newfoundland and Labrador.

Monday, July 14 at 6:00pm

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Francis Anokye

Memorial University of Newfoundland
"Quantifying the Hidden Burden of Omicron and the Impact of Alert Level System in Newfoundland and Labrador."
The Omicron wave of the coronavirus disease 2019 (COVID-19) pandemic posed significant challenges for public health interventions and surveillance efforts due to limited diagnostic capacity to detect all infections. Newfoundland and Labrador (NL), a Canadian province, transitioned between its tiered Alert Level System (ALS) to guide the intensity of non-pharmaceutical interventions (NPIs) as cases surged. However, the true burden of infections and impact of the provincial ALS on Omicron transmission remains unclear due to widespread underreporting driven by restricted access to reverse transcription polymerase chain reaction (RT-PCR) testing. This study resolves the challenge of quantifying Omicron's transmission under conditions of limited testing and surveillance gaps in NL by estimating the true burden of infection using a calibrated mechanistic compartmental model, fit to infection-induced seroprevalence data. By integrating time-varying testing eligibility fractions and estimating transmission parameters, we quantify how the provincial ALS influenced viral transmission between December 15, 2021, and May 26, 2022. Our findings reveal that alert level 4 (ALS-4; the most restrictive under Omicron) was associated with an 85% reduction in Omicron's transmission and marked the only phase in which Omicron incidence decreased while the less restrictive levels rather slowed transmission. The estimated burden of infections (182,534) was over four times the number of reported RT-PCR confirmed cases (41,619), highlighting about 77% substantial under-ascertainment rate. These results contribute to rare empirical evidence that stringent public health restrictions can suppress Omicron transmission in highly vaccinated populations, an outcome that has been observed in only a few jurisdictions and underscores the importance of seroprevalence-informed modelling for policy evaluation.



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