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

PLoS Comput Biol. 2022 Jul 8;18(7):e1010237. doi: 10.1371/journal.pcbi.1010237. eCollection 2022 Jul.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19 Vaccines*
  • COVID-19* / epidemiology
  • COVID-19* / prevention & control
  • Humans
  • Immunization Programs
  • SARS-CoV-2
  • Vaccination / methods

Substances

  • COVID-19 Vaccines

Grants and funding

JCL and AR acknowledge funding from the Swiss National Science Foundation via the project “Optimal control of intervention strategies for waterborne disease epidemics” (200021--172578). AR, EB and DP acknowledge funding from Fondazione Cassa di Risparmio di Padova e Rovigo (IT) through its grant 55722. DP, EB, LM, RC, MZ and JCL thanks the FISR founding for the project EPIDOC (Epidemiological Data assimilation and Optimal Control for short-term forecasting and emergency management of COVID-19 in Italy), FISR-2020IP-04249. DP acknowledges funding from the Ca’ Foscari University of Venice (“Fondi di primo insediamento"). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.