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research article

Optimal control of the spatial allocation of COVID-19 vaccines: Italy as a case study

Rinaldo, Andrea  
•
Lemaitre, Joseph Chadi  
•
Pasetto, Damiano
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July 8, 2022
PLoS Computational Biology

While campaigns of vaccination against SARS-CoV-2 are underway across the world, communities face the challenge of a fair and effective distribution of a limited supply of doses. Current vaccine allocation strategies are based on criteria such as age or risk. In the light of strong spatial heterogeneities in disease history and transmission, we explore spatial allocation strategies as a complement to existing approaches. Given the practical constraints and complex epidemiological dynamics, designing effective vaccination strategies at a country scale is an intricate task. We propose a novel optimal control framework to derive the best possible vaccine allocation for given disease transmission projections and constraints on vaccine supply and distribution logistics. As a proof-of-concept, we couple our framework with an existing spatially explicit compartmental COVID-19 model tailored to the Italian geographic and epidemiological context. We optimize the vaccine allocation on scenarios of unfolding disease transmission across the 107 provinces of Italy, from January to April 2021. For each scenario, the optimal solution significantly outperforms alternative strategies that prioritize provinces based on incidence, population distribution, or prevalence of susceptibles. Our results suggest that the complex interplay between the mobility network and the spatial heterogeneities implies highly non-trivial prioritization strategies for effective vaccination campaigns. Our work demonstrates the potential of optimal control for complex and heterogeneous epidemiological landscapes at country, and possibly global, scales.

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Type
research article
DOI
10.1371/journal.pcbi.1010237
Author(s)
Rinaldo, Andrea  
Lemaitre, Joseph Chadi  
Pasetto, Damiano
Zanon, Mario
Bertuzzo, Enrico
Mari, Lorenzo
Miccoli, Stefano
Casagrandi, Renato
Gatto, Marino
Date Issued

2022-07-08

Published in
PLoS Computational Biology
Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
ECHO  
Available on Infoscience
March 7, 2023
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/195520
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