Optimal control of the spatial allocation of COVID-19 vaccines: Italy as a case study
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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.
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SciScore for 10.1101/2021.05.06.21256732: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:However limitations in vaccine supply and rate of delivery are a reality for every country, hence the available doses should be deployed in space and time following a fair and effective strategy. In stockpile-limited settings, like most current vaccination campaigns worldwide, careful allocation may significantly increase the number of averted infections and deaths. The goal is to distribute the vaccines where they have the strongest …
SciScore for 10.1101/2021.05.06.21256732: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:However limitations in vaccine supply and rate of delivery are a reality for every country, hence the available doses should be deployed in space and time following a fair and effective strategy. In stockpile-limited settings, like most current vaccination campaigns worldwide, careful allocation may significantly increase the number of averted infections and deaths. The goal is to distribute the vaccines where they have the strongest beneficial impact on the dynamics of the epidemic. However, deriving an algorithm capable of computing spatially optimal allocation strategies in real, heterogeneous settings is far from trivial and our approach is, to the best of our knowledge, the first attempt in this direction. We developed an novel optimal control framework that delivers the best vaccination strategy under constraints on supply and logistics. This allows us to compute the allocation strategy that maximizes the number of averted infections during a projection of the COVID-19 epidemic in Italy from January 11, 2021 to April 11, 2021. Our results show that the optimal strategy has a complex structure that mainly reflects the projected incidence of each province, but also takes into account the spatial connectivity provided by the mobility network and the landscape of acquired population immunity. Although the reason why this strategy is optimal is not immediately intuitive, our simulations clearly outline its better overall performances over other, more straightforward strategi...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- No funding statement was detected.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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