Mathematical modelling of vaccination rollout and NPIs lifting on COVID-19 transmission with VOC: a case study in Toronto, Canada

Elena Aruffo; Pei Yuan; Yi Tan; Evgenia Gatov; Iain Moyles; Jacques Bélair; James Watmough; Sarah Collier; Julien Arino; Huaiping Zhu

doi:10.1186/s12889-022-13597-9

Mathematical modelling of vaccination rollout and NPIs lifting on COVID-19 transmission with VOC: a case study in Toronto, Canada

BMC Public Health. 2022 Jul 15;22(1):1349. doi: 10.1186/s12889-022-13597-9.

Authors

Elena Aruffo^#^{1

2}, Pei Yuan^#^{1

2}, Yi Tan^#^{1

2}, Evgenia Gatov³, Iain Moyles^{1

2}, Jacques Bélair^{1

4}, James Watmough^{1

5}, Sarah Collier³, Julien Arino^{1

6}, Huaiping Zhu^{7

8}

Affiliations

¹ Centre for Disease Modelling (CDM), York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada.
² Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada.
³ Toronto Public Health, City of Toronto, Toronto, ON, Canada.
⁴ Département de Mathématiques Et de Statistique, Université de Montréal, Montréal, Québec, Canada.
⁵ Department of Mathematics and Statistics, University of New Brunswick, Fredericton, New Brunswick, Canada.
⁶ Department of Mathematics, University of Manitoba, Winnipeg, MB, Canada.
⁷ Centre for Disease Modelling (CDM), York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada. huaiping@yorku.ca.
⁸ Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada. huaiping@yorku.ca.

^# Contributed equally.

Abstract

Background: Since December 2020, public health agencies have implemented a variety of vaccination strategies to curb the spread of SARS-CoV-2, along with pre-existing Nonpharmaceutical Interventions (NPIs). Initial strategies focused on vaccinating the elderly to prevent hospitalizations and deaths, but with vaccines becoming available to the broader population, it became important to determine the optimal strategy to enable the safe lifting of NPIs while avoiding virus resurgence.

Methods: We extended the classic deterministic SIR compartmental disease-transmission model to simulate the lifting of NPIs under different vaccine rollout scenarios. Using case and vaccination data from Toronto, Canada between December 28, 2020, and May 19, 2021, we estimated transmission throughout past stages of NPI escalation/relaxation to compare the impact of lifting NPIs on different dates on cases, hospitalizations, and deaths, given varying degrees of vaccine coverages by 20-year age groups, accounting for waning immunity.

Results: We found that, once coverage among the elderly is high enough (80% with at least one dose), the main age groups to target are 20-39 and 40-59 years, wherein first-dose coverage of at least 70% by mid-June 2021 is needed to minimize the possibility of resurgence if NPIs are to be lifted in the summer. While a resurgence was observed for every scenario of NPI lifting, we also found that under an optimistic vaccination coverage (70% coverage by mid-June, along with postponing reopening from August 2021 to September 2021) can reduce case counts and severe outcomes by roughly 57% by December 31, 2021.

Conclusions: Our results suggest that focusing the vaccination strategy on the working-age population can curb the spread of SARS-CoV-2. However, even with high vaccination coverage in adults, increasing contacts and easing protective personal behaviours is not advisable since a resurgence is expected to occur, especially with an earlier reopening.

Keywords: Age structure; COVID-19; Mathematical modeling; Nonpharmaceutical Interventions; Resurgence; SARS-CoV-2; VOC; Vaccine; Waning.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adult
Aged
COVID-19* / epidemiology
COVID-19* / prevention & control
Canada / epidemiology
Humans
Models, Theoretical
SARS-CoV-2
Vaccination