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Article

COVID-19 Seroprevalence in Canada Modelling Waning and Boosting COVID-19 Immunity in Canada a Canadian Immunization Research Network Study

1
Mathematics and Statistics, Centre for Disease Modelling, York University, Toronto, ON M3J 1P3, Canada
2
Department of Mathematics, Virginia Tech, Blacksburg, VA 24061, USA
3
Department of Mathematics, Purdue University, West Lafayette, IN 46202, USA
4
National Science Foundation, Alexandria, VA 22314, USA
5
Department of Mathematics, California State University, Northridge, CA 91330, USA
6
Department of Mathematics, University of Szeged, 6720 Szeged, Hungary
7
Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montreal, QC H3A 0G4, Canada
8
National Microbiology Laboratory, Public Health Risk Sciences Division, Public Health Agency of Canada, St. Hyacinthe, QC J2S 2M2, Canada
*
Author to whom correspondence should be addressed.
Vaccines 2022, 10(1), 17; https://doi.org/10.3390/vaccines10010017
Submission received: 25 September 2021 / Revised: 23 October 2021 / Accepted: 24 November 2021 / Published: 23 December 2021

Abstract

COVID-19 seroprevalence changes over time, with infection, vaccination, and waning immunity. Seroprevalence estimates are needed to determine when increased COVID-19 vaccination coverage is needed, and when booster doses should be considered, to reduce the spread and disease severity of COVID-19 infection. We use an age-structured model including infection, vaccination and waning immunity to estimate the distribution of immunity to COVID-19 in the Canadian population. This is the first mathematical model to do so. We estimate that 60–80% of the Canadian population has some immunity to COVID-19 by late Summer 2021, depending on specific characteristics of the vaccine and the waning rate of immunity. Models results indicate that increased vaccination uptake in age groups 12–29, and booster doses in age group 50+ are needed to reduce the severity COVID-19 Fall 2021 resurgence.
Keywords: seroprevalence; COVID-19; infectious disease modelling; mathematical epidmiology seroprevalence; COVID-19; infectious disease modelling; mathematical epidmiology

Share and Cite

MDPI and ACS Style

Dick, D.W.; Childs, L.; Feng, Z.; Li, J.; Röst, G.; Buckeridge, D.L.; Ogden, N.H.; Heffernan, J.M. COVID-19 Seroprevalence in Canada Modelling Waning and Boosting COVID-19 Immunity in Canada a Canadian Immunization Research Network Study. Vaccines 2022, 10, 17. https://doi.org/10.3390/vaccines10010017

AMA Style

Dick DW, Childs L, Feng Z, Li J, Röst G, Buckeridge DL, Ogden NH, Heffernan JM. COVID-19 Seroprevalence in Canada Modelling Waning and Boosting COVID-19 Immunity in Canada a Canadian Immunization Research Network Study. Vaccines. 2022; 10(1):17. https://doi.org/10.3390/vaccines10010017

Chicago/Turabian Style

Dick, David W., Lauren Childs, Zhilan Feng, Jing Li, Gergely Röst, David L. Buckeridge, Nick H. Ogden, and Jane M. Heffernan. 2022. "COVID-19 Seroprevalence in Canada Modelling Waning and Boosting COVID-19 Immunity in Canada a Canadian Immunization Research Network Study" Vaccines 10, no. 1: 17. https://doi.org/10.3390/vaccines10010017

APA Style

Dick, D. W., Childs, L., Feng, Z., Li, J., Röst, G., Buckeridge, D. L., Ogden, N. H., & Heffernan, J. M. (2022). COVID-19 Seroprevalence in Canada Modelling Waning and Boosting COVID-19 Immunity in Canada a Canadian Immunization Research Network Study. Vaccines, 10(1), 17. https://doi.org/10.3390/vaccines10010017

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