Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Dec 12, 2020
Open Peer Review Period: Dec 10, 2020 - Dec 24, 2020
Date Accepted: Jun 22, 2021
Date Submitted to PubMed: Aug 3, 2021
(closed for review but you can still tweet)
A model-based estimation of the COVID-19 period prevalence and undiagnosed population in Canadian provinces
ABSTRACT
Background:
The development of a successful COVID-19 control strategy requires a thorough understanding of the trends in the geographic and demographic distributions of the disease burden. In terms of the estimation of the population prevalence, this includes the crucial process of unravelling the number of patients who remain undiagnosed.
Objective:
This study estimates the prevalence and undiagnosed proportion of COVID-19 in Quebec, Ontario, Alberta, and British Columbia, Canada using a model-based approach, informed by provincial data.
Methods:
A model-based mathematical framework based on a disease progression and transmission model was developed to estimate the historical prevalence of COVID-19 using reported provincial data. The framework was applied to three different age cohorts (under 30; 30–69; and 70+) in each of the provinces studied.
Results:
The estimates of COVID-19 period prevalence between March 1, 2020 to November 30, 2020 were 4.73% (4.42%-4.99%), 2.88% (2.75%-3.02%), 3.27% (2.72%-3.70%), 2.95% (2.77%-3.15%) for Quebec, Ontario, Alberta, and British Columbia, respectively. Those values are 3–6 fold the reported number of diagnosed cases in the provinces.
Conclusions:
Our study extrapolates the estimates of previous COVID-19 seroprevalence studies by integrating a natural history and transmission model with population-level reported data on COVID-19-related health events. This approach provides continuous updates of the COVID-19 period prevalence when no recent seroprevalence data is available. Knowledge of COVID-19 period prevalence can provide vital evidence for policy makers to consider when planning COVID-19 control interventions and vaccination programs.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.