Prioritising COVID-19 vaccination in changing social and epidemiological landscapes
Bibliographic record
Abstract
Summary Background During 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. Methods We 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. Findings Case 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. Interpretation Further research is urgently needed to determine which populations can benefit from using SARS-CoV-2 vaccines to interrupt transmission. Funding Ontario Ministry of Colleges and Universities. Research in context Evidence before this study Whether 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 study The 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 evidence Seroprevalence 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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".