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Prioritising COVID-19 vaccination in changing social and epidemiological landscapes: a mathematical modelling study.
Lancet Infect Dis. 2021 08; 21(8):1097-1106.LI

Abstract

BACKGROUND

During the COVID-19 pandemic, authorities must decide which groups to prioritise for vaccination in a shifting social-epidemiological landscape in which the success of large-scale non-pharmaceutical interventions requires broad social acceptance. We aimed to compare projected COVID-19 mortality under four different strategies for the prioritisation of SARS-CoV-2 vaccines.

METHODS

We developed a coupled social-epidemiological model of SARS-CoV-2 transmission in which social and epidemiological dynamics interact with one another. We modelled how population adherence to non-pharmaceutical interventions responds to case incidence. In the model, schools and workplaces are also closed and reopened on the basis of reported cases. The model was parameterised with data on COVID-19 cases and mortality, SARS-CoV-2 seroprevalence, population mobility, and demography from Ontario, Canada (population 14·5 million). Disease progression parameters came from the SARS-CoV-2 epidemiological literature. We assumed a vaccine with 75% efficacy against disease and transmissibility. We compared vaccinating those aged 60 years and older first (oldest-first strategy), vaccinating those younger than 20 years first (youngest-first strategy), vaccinating uniformly by age (uniform strategy), and a novel contact-based strategy. The latter three strategies interrupt transmission, whereas the first targets a vulnerable group to reduce disease. Vaccination rates ranged from 0·5% to 5% of the population per week, beginning on either Jan 1 or Sept 1, 2021.

FINDINGS

Case notifications, non-pharmaceutical intervention adherence, and lockdown undergo successive waves that interact with the timing of the vaccine programme to determine the relative effectiveness of the four strategies. Transmission-interrupting strategies become relatively more effective with time as herd immunity builds. The model predicts that, in the absence of vaccination, 72 000 deaths (95% credible interval 40 000-122 000) would occur in Ontario from Jan 1, 2021, to March 14, 2025, and at a vaccination rate of 1·5% of the population per week, the oldest-first strategy would reduce COVID-19 mortality by 90·8% on average (followed by 89·5% in the uniform, 88·9% in the contact-based, and 88·2% in the youngest-first strategies). 60 000 deaths (31 000-108 000) would occur from Sept 1, 2021, to March 14, 2025, in the absence of vaccination, and the contact-based strategy would reduce COVID-19 mortality by 92·6% on average (followed by 92·1% in the uniform, 91·0% in the oldest-first, and 88·3% in the youngest-first strategies) at a vaccination rate of 1·5% of the population per week.

INTERPRETATION

The most effective vaccination strategy for reducing mortality due to COVID-19 depends on the time course of the pandemic in the population. For later vaccination start dates, use of SARS-CoV-2 vaccines to interrupt transmission might prevent more deaths than prioritising vulnerable age groups.

FUNDING

Ontario Ministry of Colleges and Universities.

Authors+Show Affiliations

Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada; School of Environmental Sciences, University of Guelph, Guelph, ON, Canada.School of Environmental Sciences, University of Guelph, Guelph, ON, Canada.Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada. Electronic address: cbauch@uwaterloo.ca.

Pub Type(s)

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

Language

eng

PubMed ID

33811817

Citation

Jentsch, Peter C., et al. "Prioritising COVID-19 Vaccination in Changing Social and Epidemiological Landscapes: a Mathematical Modelling Study." The Lancet. Infectious Diseases, vol. 21, no. 8, 2021, pp. 1097-1106.
Jentsch PC, Anand M, Bauch CT. Prioritising COVID-19 vaccination in changing social and epidemiological landscapes: a mathematical modelling study. Lancet Infect Dis. 2021;21(8):1097-1106.
Jentsch, P. C., Anand, M., & Bauch, C. T. (2021). Prioritising COVID-19 vaccination in changing social and epidemiological landscapes: a mathematical modelling study. The Lancet. Infectious Diseases, 21(8), 1097-1106. https://doi.org/10.1016/S1473-3099(21)00057-8
Jentsch PC, Anand M, Bauch CT. Prioritising COVID-19 Vaccination in Changing Social and Epidemiological Landscapes: a Mathematical Modelling Study. Lancet Infect Dis. 2021;21(8):1097-1106. PubMed PMID: 33811817.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Prioritising COVID-19 vaccination in changing social and epidemiological landscapes: a mathematical modelling study. AU - Jentsch,Peter C, AU - Anand,Madhur, AU - Bauch,Chris T, Y1 - 2021/03/31/ PY - 2020/09/25/received PY - 2021/01/07/revised PY - 2021/01/22/accepted PY - 2021/4/4/pubmed PY - 2021/8/11/medline PY - 2021/4/3/entrez SP - 1097 EP - 1106 JF - The Lancet. Infectious diseases JO - Lancet Infect Dis VL - 21 IS - 8 N2 - BACKGROUND: During the COVID-19 pandemic, authorities must decide which groups to prioritise for vaccination in a shifting social-epidemiological landscape in which the success of large-scale non-pharmaceutical interventions requires broad social acceptance. We aimed to compare projected COVID-19 mortality under four different strategies for the prioritisation of SARS-CoV-2 vaccines. METHODS: We developed a coupled social-epidemiological model of SARS-CoV-2 transmission in which social and epidemiological dynamics interact with one another. We modelled how population adherence to non-pharmaceutical interventions responds to case incidence. In the model, schools and workplaces are also closed and reopened on the basis of reported cases. The model was parameterised with data on COVID-19 cases and mortality, SARS-CoV-2 seroprevalence, population mobility, and demography from Ontario, Canada (population 14·5 million). Disease progression parameters came from the SARS-CoV-2 epidemiological literature. We assumed a vaccine with 75% efficacy against disease and transmissibility. We compared vaccinating those aged 60 years and older first (oldest-first strategy), vaccinating those younger than 20 years first (youngest-first strategy), vaccinating uniformly by age (uniform strategy), and a novel contact-based strategy. The latter three strategies interrupt transmission, whereas the first targets a vulnerable group to reduce disease. Vaccination rates ranged from 0·5% to 5% of the population per week, beginning on either Jan 1 or Sept 1, 2021. FINDINGS: Case notifications, non-pharmaceutical intervention adherence, and lockdown undergo successive waves that interact with the timing of the vaccine programme to determine the relative effectiveness of the four strategies. Transmission-interrupting strategies become relatively more effective with time as herd immunity builds. The model predicts that, in the absence of vaccination, 72 000 deaths (95% credible interval 40 000-122 000) would occur in Ontario from Jan 1, 2021, to March 14, 2025, and at a vaccination rate of 1·5% of the population per week, the oldest-first strategy would reduce COVID-19 mortality by 90·8% on average (followed by 89·5% in the uniform, 88·9% in the contact-based, and 88·2% in the youngest-first strategies). 60 000 deaths (31 000-108 000) would occur from Sept 1, 2021, to March 14, 2025, in the absence of vaccination, and the contact-based strategy would reduce COVID-19 mortality by 92·6% on average (followed by 92·1% in the uniform, 91·0% in the oldest-first, and 88·3% in the youngest-first strategies) at a vaccination rate of 1·5% of the population per week. INTERPRETATION: The most effective vaccination strategy for reducing mortality due to COVID-19 depends on the time course of the pandemic in the population. For later vaccination start dates, use of SARS-CoV-2 vaccines to interrupt transmission might prevent more deaths than prioritising vulnerable age groups. FUNDING: Ontario Ministry of Colleges and Universities. SN - 1474-4457 UR - https://www.unboundmedicine.com/medline/citation/33811817/Prioritising_COVID_19_vaccination_in_changing_social_and_epidemiological_landscapes:_a_mathematical_modelling_study_ DB - PRIME DP - Unbound Medicine ER -