COVID-19 vaccines have been authorized in multiple countries, and more are under rapid development. Careful design of a vaccine prioritization strategy across sociodemographic groups is a crucial public policy challenge given that 1) vaccine supply will be constrained for the first several months of the vaccination campaign, 2) there are stark differences in transmission and severity of impacts from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across groups, and 3) SARS-CoV-2 differs markedly from previous pandemic viruses. We assess the optimal allocation of a limited vaccine supply in the United States across groups differentiated by age and essential worker status, which constrains opportunities for social distancing. We model transmission dynamics using a compartmental model parameterized to capture current understanding of the epidemiological characteristics of COVID-19, including key sources of group heterogeneity (susceptibility, severity, and contact rates). We investigate three alternative policy objectives (minimizing infections, years of life lost, or deaths) and model a dynamic strategy that evolves with the population epidemiological status. We find that this temporal flexibility contributes substantially to public health goals. Older essential workers are typically targeted first. However, depending on the objective, younger essential workers are prioritized to control spread or seniors to directly control mortality. When the objective is minimizing deaths, relative to an untargeted approach, prioritization averts deaths on a range between 20,000 (when nonpharmaceutical interventions are strong) and 300,000 (when these interventions are weak). We illustrate how optimal prioritization is sensitive to several factors, most notably, vaccine effectiveness and supply, rate of transmission, and the magnitude of initial infections.

Multiple promising COVID-19 vaccines are under rapid development, with deployment of the initial supply expected by 2021. Careful design of a vaccine prioritization strategy across socio-demographic groups is an imminent and crucial public policy challenge given that (1) the eventual vaccine supply will be highly constrained for at least the first several months of the vaccination campaign, and (2) there are stark differences in transmission and severity of impacts from SARS-CoV-2 across groups. Previous experience with vaccine development mid-pandemic offers limited insights for SARS-CoV-2 prioritization: SARS and Zika vaccine development was incomplete when those outbreaks ended and the epidemiology of endemic human influenza viruses differ substantially from that of SARS-CoV-2. We assess the optimal allocation of a limited and dynamic COVID-19 vaccine supply in the U.S. across socio-demographic groups differentiated by age and essential worker status. The transmission dynamics are modeled using a compartmental (SEIR) model parameterized to capture our current understanding of the transmission and epidemiological characteristics of COVID-19, including key sources of group heterogeneity (susceptibility, severity, and contact rates). We investigate tradeoffs between three alternative policy objectives: minimizing infections, years of life lost, or deaths. Moreover, we model dynamic vaccine prioritization policies that respond to changes in the epidemiological status of the population as SARS-CoV-2 continues its march. Because contacts tend to be concentrated within age groups, there is diminishing marginal returns as vaccination coverage increases in a given group, increasing the group's protective immunity against infection and mortality. We find that optimal prioritization consistently targets older essential workers. However, depending on the policy objective, younger essential workers are prioritized to minimize infections or seniors in order to minimize mortality. Optimal prioritization outperforms non-targeted vaccination strategies by up to 18% depending on the outcome optimized. For example, in our baseline model, cumulative mortality decreased on average by 17% (25,000 deaths in the U.S. population) over the course of the outbreak.

Fish in all the world’s oceans exhibit variable body size and growth over time, with some species or populations exhibiting long-term declines in size. These patterns can be caused by a range of biotic, abiotic, and anthropogenic factors and impact the productivity of harvested populations. Within a given species, individuals often exhibit a range of life-history strategies that may cause some groups to be buffered against change. One of the most studied declines in size at age has been in populations of salmon; Chinook salmon in the Northeast Pacific Ocean are the largest bodied salmon species and have experienced long-term declines in size. Using long-term monitoring data, we develop novel size and growth models to link observed changes in Chinook size to life history traits and environmental variability. Our results identify three distinct trends in size across the 48 stocks in our study. Differences among populations were correlated with ocean distribution, migration timing, and freshwater residence. We provide evidence that trends are driven by interannual variation in certain oceanographic processes and competition with pink salmon.