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Reviews: "Prioritising COVID-19 Vaccination in Changing Social and Epidemiological Landscapes"

Reviewers: J Gomez- Gardenes (University of Zaragoza) |📒📒📒 ◻️◻️

Published onMar 09, 2022
Reviews: "Prioritising COVID-19 Vaccination in Changing Social and Epidemiological Landscapes"
key-enterThis Pub is a Review of
Prioritising COVID-19 vaccination in changing social and epidemiological landscapes

SummaryBackgroundDuring the COVID-19 pandemic, authorities must decide which groups to prioritise for vaccination. These decision will occur in a constantly shifting social-epidemiological landscape where the success of large-scale non-pharmaceutical interventions (NPIs) like physical distancing requires broad population acceptance.MethodsWe developed a coupled social-epidemiological model of SARS-CoV-2 transmission. Schools and workplaces are closed and re-opened based on reported cases. We used evolutionary game theory and mobility data to model individual adherence to NPIs. We explored the impact of vaccinating 60+ year-olds first; <20 year-olds first; uniformly by age; and a novel contact-based strategy. The last three strategies interrupt transmission while the first targets a vulnerable group. Vaccination rates ranged from 0.5% to 4.5% of the population per week, beginning in January or July 2021.FindingsCase notifications, NPI adherence, and lockdown periods undergo successive waves during the simulated pandemic. Vaccination reduces median deaths by 32% – 77% (22% – 63%) for January (July) availability, depending on the scenario. Vaccinating 60+ year-olds first prevents more deaths (up to 8% more) than transmission-interrupting strategies for January vaccine availability across most parameter regimes. In contrast, transmission-interrupting strategies prevent up to 33% more deaths than vaccinating 60+ year-olds first for July availability, due to higher levels of natural immunity by that time. Sensitivity analysis supports the findings.InterpretationFurther research is urgently needed to determine which populations can benefit from using SARS-CoV-2 vaccines to interrupt transmission.FundingOntario Ministry of Colleges and Universities.Research in contextEvidence before this studyWhether to vaccinate individuals who cause the most transmission or those who are at highest risk of death is relevant to prioritizing COVID-19 vaccination. We searched PubMed and medRxiv for the terms COVID19, vaccin*, model, and priorit* up to September 24, 2020, with no date or language restrictions. We identified 4 papers on mathematical models of COVID-19 vaccine prioritization that explored the conditions under which different age groups should be vaccinated first. We did not find any coupled social-epidemiological models that capture feedback between social dynamics and epidemic trajectories.Added value of this studyThe dynamic interaction between SARS-CoV-2 epidemics and the population response through scalable non-pharmaceutical interventions will continue to play a large role in the course of the pandemic, both before and after vaccines become available. Hence, social-epidemiological models may be useful. Our social-epidemiological model identifies the conditions under which COVID-19 deaths can be reduced most effectively by prioritizing older individuals first, versus other strategies designed to interrupt transmission. We explore how the best vaccination strategy varies depending on a wide range of socio-epidemiological and vaccine program parameters. We identify clear and interpretable conditions under which using COVID-19 vaccines to interrupt transmission can reduce mortality most effectively.Implications of all the available evidenceSeroprevalence surveys before the onset of vaccination could measure population-level SARS-CoV-2 immunity. In populations where seropositivity is high due to previous waves, vaccinating to interrupt transmission may reduce deaths more effectively than targeting older individuals. More research is urgently required to evaluate how to prioritise vaccination in populations that have experienced one or more waves of COVID-19.

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Reviewer 1 (Jesus Gomez- G…) | 📒📒📒 ◻️◻️

RR:C19 Strength of Evidence Scale Key

📕 ◻️◻️◻️◻️ = Misleading

📙📙 ◻️◻️◻️ = Not Informative

📒📒📒 ◻️◻️ = Potentially Informative

📗📗📗📗◻️ = Reliable

📘📘📘📘📘 = Strong

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