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Adaptive and optimized COVID-19 vaccination strategies across geographical regions and age groups

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journal contribution
posted on 11.05.2022, 10:29 by Jeta Molla, Alejandro Ponce de León Chávez, Takayuki Hiraoka, Tapio Ala-NissilaTapio Ala-Nissila, Mikko Kivelä, Lasse Leskelä
We evaluate the efficiency of various heuristic strategies for allocating vaccines against COVID-19 and compare them to strategies found using optimal control theory. Our approach is based on a mathematical model which tracks the spread of disease among different age groups and across different geographical regions, and we introduce a method to combine age-specific contact data to geographical movement data. As a case study, we model the epidemic in the population of mainland Finland utilizing mobility data from a major telecom operator. Our approach allows to determine which geographical regions and age groups should be targeted first in order to minimize the number of deaths. In the scenarios that we test, we find that distributing vaccines demographically and in an age-descending order is not optimal for minimizing deaths and the burden of disease. Instead, more lives could be saved by using strategies which emphasize high-incidence regions and distribute vaccines in parallel to multiple age groups. The level of emphasis that high-incidence regions should be given depends on the overall transmission rate in the population. This observation highlights the importance of updating the vaccination strategy when the effective reproduction number changes due to the general contact patterns changing and new virus variants entering.

Funding

NordForsk and the Academy of Finland through its PolyDyna grant no. 307806

History

School

  • Science

Department

  • Mathematical Sciences

Published in

PLoS Computational Biology

Volume

18

Issue

4

Publisher

Public Library of Science (PLoS)

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by Public Library of Science (PLoS) under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

28/02/2022

Publication date

2022-04-07

Copyright date

2022

ISSN

1553-734X

eISSN

1553-7358

Language

en

Depositor

Prof Tapio Ala-Nissila. Deposit date: 10 May 2022

Article number

e1009974