Optimal Scheduling of Anticipated COVID-19 Vaccination: A Case Study of New York State
Abstract
This study aims to determine an optimal control strategy for vaccine scheduling in COVID-19 pandemic treatment by converting widely acknowledged infectious disease model named SEIR into an optimal control problem. The problem is augmented by adding medication and vaccine limitations to match real-world situations. Two version of the problem is formulated to minimize the number of infected individuals at the same provide the optimal vaccine possible to reduce the susceptible population to a considerably lower state. Optimal control problems are solved using RBF-Galerkin method. These problems are tested with a benchmarking dataset to determine required parameters. After this step, problems are tested with recent data for New York State, USA. The results regarding the proposed optimal control problem provides a set of evidences from which an optimal strategy for vaccine scheduling can be chosen, when the vaccine for COVID-19 will be available.
Cite
@article{arxiv.2008.10702,
title = {Optimal Scheduling of Anticipated COVID-19 Vaccination: A Case Study of New York State},
author = {Syed Irfan Ali Meerza and Seyed M. Karimi and Bert B. Little and Jacek M. Zurada and Tamer Inanc},
journal= {arXiv preprint arXiv:2008.10702},
year = {2020}
}
Comments
Submitted to International conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)-2020