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

Qi Luo (1) , Ryan Weightman (2) , Sean Mcquade (2) , Mateo Díaz (3) , Emmanuel Trélat (4, 5) , William Barbour (6) , Dan Work (6) , Samitha Samaranayake (7) , Benedetto Piccoli (2)

1 Clemson University
2 CCIB - Center for Computational and Integrative Biology [Camden]
3 Department of Computing and Mathematical sciences
4 CaGE - Control And GEometry
5 LJLL (UMR_7598) - Laboratoire Jacques-Louis Lions
6 Vanderbilt University [Nashville]
7 Cornell University [New York]

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.

Mots clés: COVID-19; Optimal control; SARS-CoV-2; SEIR compartmental models; Vaccine

Domaines: Optimisation et contrôle [math.OC]

Qi Luo, Ryan Weightman, Sean Mcquade, Mateo Díaz, Emmanuel Trélat, et al.. Optimization of vaccination for COVID-19 in the midst of a pandemic. Networks and Heterogeneous Media, 2022, 17 (3), pp.443-466. ⟨10.3934/nhm.2022016⟩. ⟨hal-03669889⟩