Dynamic Prioritization of COVID-19 Vaccines When Social Distancing is Limited for Essential Workers

Abstract

COVID-19 vaccines have been authorized in multiple countries and more are under rapid development. Careful design of a vaccine prioritization strategy across socio-demographic 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 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 U.S. across groups differentiated by age and also 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 non-pharmaceutical 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.

Figure 6:

The proportion of each demographic group infected when vaccine distribution begins in the Base scenario.

Figure 7:

The sensitivity of static policies to the alternative scenarios as given by the percent of the initial supply allocated to each demographic group (A), the performance relative to the optimum allocation of each policy when applied to each of the alternative scenarios when the objective is YLL (B).

Figure 8:

The percentage of additional deaths in excess of the optimum when applying a policy for an alternative scenario (row) to an alternative “true” scenario (column).

Figure 9:

The percentage of additional infections in excess of the optimum when applying a policy for an alternative scenario (row) to an alternative “true” scenario (column).

Figure 10:

The percent of each demographic group (horizontal axis) vaccinated after three months under the optimal policy for each of the alternative model structures (vertical axis) and objectives (panels).

Figure 11:

The percentage of each demographic group vaccinated after 3 months under the optimal dynamic policy given variation in (A) “other” (non-work) contacts and (B) work contacts.

Figure 1:

Schematic of the modeled movement of individuals between epidemiological states (A), the portion of individuals from the U.S. population in each demographic group determined by essential worker status (*) and age (B), and the contact rates between demographic groups, given by average daily number of contacts a group on the horizontal axis makes with a group on the vertical axis (C).

Figure 2:

The optimal allocation of vaccines (vertical axes) between demographic groups for each decision period (horizontal axis) under the Base scenario (A). The three rows represent each objective, to minimize deaths, minimize years of life lost (YLL) and minimize infections. The bars for the six decision periods show the percentage of vaccines allocated to a specific group (indicated by a letter, color, and stars indicating essential worker groups) in that period. The two final columns (B) show cumulative measures at the end of months three and six, respectively, for the percent of each group that has been vaccinated. The whiskers on each bar represent the sensitivity of the optimal solution to small deviations in the outcome, specifically the range of allocations resulting in outcomes within 0.5% of the optimal solution.

Figure 3:

The number of infections per 1,000 individuals over time under reference policies (no vaccines; untargeted vaccine allocation) and optimized policies minimizing a given metric (A); and the performance of each optimized policy relative to an untargeted allocation policy (B) for the Base scenario. The bars are boxed by each resulting metric, colored by the objective driving each policy and textured to reflect any constraint considered (e.g. age-only or static policies).

Figure 4:

The cumulative percent of each demographic group (horizontal axis) vaccinated after the first 30% of the population is vaccinated under the alternative scenarios (vertical axis) and each objective (panel) (A). The percentage of additional YLL in excess of the optimum when applying a policy for a given alternative scenario (row) when a particular scenario is the “truth” (column) (B).

Figure 5:

The total percent of each demographic group vaccinated after 3 months under the optimal dynamic policy. Each panel shows the effect of varying a key parameter relative to the Base model: (A) effectiveness of NPI, which determines the initial reproductive number (when the vaccine first becomes available); (B) monthly rate of vaccine supply; (C) initial infections; and (D) vaccine effectiveness. Base scenario parameter values are indicated with an apostrophe (‘).