Mathematical modelling of vaccination rollout and NPIs lifting on COVID-19 transmission with VOC: a case study in Toronto, Canada
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...
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| Format: | Article |
| Language: | English |
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BMC
2022-07-01
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| Series: | BMC Public Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12889-022-13597-9 |
| _version_ | 1811293709228048384 |
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| author | Elena Aruffo Pei Yuan Yi Tan Evgenia Gatov Iain Moyles Jacques Bélair James Watmough Sarah Collier Julien Arino Huaiping Zhu |
| author_facet | Elena Aruffo Pei Yuan Yi Tan Evgenia Gatov Iain Moyles Jacques Bélair James Watmough Sarah Collier Julien Arino Huaiping Zhu |
| author_sort | Elena Aruffo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T05:05:35Z |
| format | Article |
| id | doaj.art-8b46c65ee477474e9794ad8df38cd2d1 |
| institution | Directory Open Access Journal |
| issn | 1471-2458 |
| language | English |
| last_indexed | 2024-04-13T05:05:35Z |
| publishDate | 2022-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Public Health |
| spelling | doaj.art-8b46c65ee477474e9794ad8df38cd2d12022-12-22T03:01:11ZengBMCBMC Public Health1471-24582022-07-0122111210.1186/s12889-022-13597-9Mathematical modelling of vaccination rollout and NPIs lifting on COVID-19 transmission with VOC: a case study in Toronto, CanadaElena Aruffo0Pei Yuan1Yi Tan2Evgenia Gatov3Iain Moyles4Jacques Bélair5James Watmough6Sarah Collier7Julien Arino8Huaiping Zhu9Centre for Disease Modelling (CDM), York UniversityCentre for Disease Modelling (CDM), York UniversityCentre for Disease Modelling (CDM), York UniversityToronto Public Health, City of TorontoCentre for Disease Modelling (CDM), York UniversityCentre for Disease Modelling (CDM), York UniversityCentre for Disease Modelling (CDM), York UniversityToronto Public Health, City of TorontoCentre for Disease Modelling (CDM), York UniversityCentre for Disease Modelling (CDM), York UniversityAbstract 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.https://doi.org/10.1186/s12889-022-13597-9COVID-19SARS-CoV-2Mathematical modelingAge structureNonpharmaceutical InterventionsVaccine |
| spellingShingle | Elena Aruffo Pei Yuan Yi Tan Evgenia Gatov Iain Moyles Jacques Bélair James Watmough Sarah Collier Julien Arino Huaiping Zhu Mathematical modelling of vaccination rollout and NPIs lifting on COVID-19 transmission with VOC: a case study in Toronto, Canada BMC Public Health COVID-19 SARS-CoV-2 Mathematical modeling Age structure Nonpharmaceutical Interventions Vaccine |
| title | Mathematical modelling of vaccination rollout and NPIs lifting on COVID-19 transmission with VOC: a case study in Toronto, Canada |
| title_full | Mathematical modelling of vaccination rollout and NPIs lifting on COVID-19 transmission with VOC: a case study in Toronto, Canada |
| title_fullStr | Mathematical modelling of vaccination rollout and NPIs lifting on COVID-19 transmission with VOC: a case study in Toronto, Canada |
| title_full_unstemmed | Mathematical modelling of vaccination rollout and NPIs lifting on COVID-19 transmission with VOC: a case study in Toronto, Canada |
| title_short | Mathematical modelling of vaccination rollout and NPIs lifting on COVID-19 transmission with VOC: a case study in Toronto, Canada |
| title_sort | mathematical modelling of vaccination rollout and npis lifting on covid 19 transmission with voc a case study in toronto canada |
| topic | COVID-19 SARS-CoV-2 Mathematical modeling Age structure Nonpharmaceutical Interventions Vaccine |
| url | https://doi.org/10.1186/s12889-022-13597-9 |
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