Protective vaccines are the most effective tool to control SARS-CoV-2 transmission and prevent COVID-19 disease and death. Although several vaccines are now approved and in distribution, vaccine shortages are likely to occur, necessitating that vaccination is performed in a prioritized manner. However, deciding how to prioritize vaccine rollout is complicated, as the populations that should be vaccinated first vary with intended outcome, such as preventing death, preventing transmission, or preventing hospitalization. Additionally, vaccine efficacy (VE)—the ability of vaccination to prevent infection— varies between approved commercial vaccines, further complicating the decisions surrounding vaccine rollout.
Dr. Laura Matrajt and Dr. Elizabeth Brown, along with colleagues from the Vaccine and Infectious Disease Division and the University of Washington, tackled this question in a recent Science Advances article. They developed a mathematical model, that when paired with optimization algorithms, could determine the vaccine prioritization scheme for 100 combinations of VE and number of doses available. The model assumed several basic parameters, including that frontline healthcare workers were already vaccinated, susceptibility to infection increases with age, social distancing restrictions are lifted, and that immunity to infection and vaccination persists at least one year.
The authors first modeled SARS-CoV-2 mitigation and containment, finding that herd immunity will be achieved once either 60% of the population is infected or 40% of the population is vaccinated (VE of 100%), and assuming 20% of the population has already been infected and is immune. Furthermore, they modeled that the pandemic can be substantially slowed with a vaccine with a VE of at least 50%, assuming that the majority of the population is vaccinated. However, optimal vaccine coverage allocation varies with VE and coverage (based on vaccine availability): in low coverage, low-VE settings, it is necessary to vaccinate people at highest risk—those 75-and-older—first. In contrast, when there is ample vaccine to cover half the population and VE exceeds 60%, it would be best to vaccinate the high-transmission groups that drive SARS-CoV-2 spread first, and as more vaccine becomes available, to vaccinate the older age groups.
Dr. Matrajt and colleagues next modeled how objective function affects vaccine prioritization. To avoid symptomatic infections and non-ICU hospitalizations, their model found that vaccines should be given to younger populations, who have more social contacts and have higher likelihood of driving transmission. However, to avoid ICU hospitalizations and death, priority should be given to older populations, who are most at risk for severe COVID-19 disease. These discrepancies in vaccine prioritization are most apparent when vaccine coverage is low (less than 30%), while coverage of at least 60% prioritizes vaccination of high-transmission groups regardless of intended outcome.
The authors also considered the effects of several other factors on vaccine prioritization. While VE represents the ability of a vaccine to prevent infection, it is also important to understand how well a vaccine prevents symptoms of COVID-19 disease, even when a vaccine fails to prevent infection with SARS-CoV-2. The authors modeled how this metric, known as VECOV, affects vaccine prioritization. They reported that a vaccine with a strong ability to reduce symptomatic infection and disease would have a huge impact in reducing hospitalizations, even if such vaccine had a low efficacy reducing infection. In another simulation, they found that assuming increasing levels of pre-existing SARS-CoV-2 immunity, due to natural infection, at the start of vaccination mirrors the vaccination prioritization patterns observed with increasing vaccine coverage, when preventing death is the intended outcome.
Finally, the authors modeled the vaccine campaign, assuming it takes place over two years, with either 75,000, 150,000, or 300,000 doses given per week. In line with their previous findings, vaccine priority differed with vaccination rate, where older, high-risk populations should be prioritized in settings of slower rates of vaccination, but that high-transmission groups should be prioritized when vaccination rates are increased. However, for a given VE, vaccine allocation prioritization is identical beyond a certain vaccination coverage, suggesting that there is an upper limit to transmission reduction while vaccine rollout is ongoing, necessitating other interventions such as social distancing before herd immunity is reached.
This work demonstrated that depending on the intended outcome, vaccination population hierarchies differ, and that mathematical modeling can inform optimal vaccination strategies to prevent death, transmission, or hospitalizations. Encouragingly, their work shows that even a moderately effective vaccine (VE of at least 50%) could substantially slow the pandemic and relieve pressure on healthcare systems, while a vaccine with VE above 70% could fully contain the spread of SARS-CoV-2. While this work presents an objective framework for vaccine allocation strategies, the authors recognize that ethical, political, and societal factors should be considered in real-life vaccination scenarios. Going forward, further work is needed to model how increased risk of infection due to occupation and increased risk of infection and death in minority communities affects vaccination prioritization.
Matrajt L, Eaton J, Leung T, Brown ER. Vaccine optimization for COVID-19: Who to vaccinate first? Science Advances. Science Advances 03 Feb 2021: Vol. 7, no. 6, eabf1374. DOI: 10.1126/sciadv.abf1374
This work was supported by the National Institute of Allergy and Infectious Disease and the National Institutes of Health.