Optimization of vaccination for COVID-19 in the midst of a pandemic

TL;DR

This paper develops an optimal control model to determine the best COVID-19 vaccination prioritization strategy, finding that vaccinating the eldest first minimizes deaths, with real-world data application and estimates of lives saved.

Contribution

It introduces an age-structured optimal control model incorporating vaccinated infection, identifying the eldest-to-youngest vaccination policy as optimal for reducing mortality.

Findings

Eldest to youngest vaccination minimizes deaths.

Model applied to US Census data for New Jersey and Florida.

Estimates of lives saved by optimized vaccination schedule.

Abstract

During the Covid-19 pandemic a key role is played by vaccination to combat the virus. There are many possible policies for prioritizing vaccines, and different criteria for optimization: minimize death, time to herd immunity, functioning of the health system. Using an age-structured population compartmental finite-dimensional optimal control model, our results suggest that the eldest to youngest vaccination policy is optimal to minimize deaths. Our model includes the possible infection of vaccinated populations. We apply our model to real-life data from the US Census for New Jersey and Florida, which have a significantly different population structure. We also provide various estimates of the number of lives saved by optimizing the vaccine schedule and compared to no vaccination.