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Optimal COVID-19 Vaccination Strategies with Limited Vaccine and Delivery Capabilities: A Model Based on Social Networks

Published:14 September 2021Publication History
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Abstract

We develop a model of infection spread that takes into account the existence of a vulnerable group as well as the variability of the social relations of individuals. We develop a compartmentalized power-law model, with power-law connections between the vulnerable and the general population, considering these connections as well as the connections among the vulnerable as parameters that we vary in our tests.

We use the model to study a number of vaccination strategies under two hypotheses: first, we assume a limited availability of vaccine but an infinite vaccination capacity, so all the available doses can be administered in a short time (negligible with respect to the evolution of the epidemic). Then, we assume a limited vaccination capacity, so the doses are administered in a time non-negligible with respect to the evolution of the epidemic.

We develop optimal strategies for the various social parameters, where a strategy consists of (1) the fraction of vaccine that is administered to the vulnerable population and (2) the criterion that is used to administer it to the general population. In the case of a limited vaccination capacity, the fraction (1) is a function of time, and we study how to optimize it to obtain a maximal reduction in the number of fatalities.

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        • Published in

          cover image ACM Transactions on Computing for Healthcare
          ACM Transactions on Computing for Healthcare  Volume 2, Issue 4
          October 2021
          199 pages
          ISSN:2691-1957
          EISSN:2637-8051
          DOI:10.1145/3476827
          Issue’s Table of Contents

          Copyright © 2021 ACM

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 14 September 2021
          • Accepted: 1 May 2021
          • Revised: 1 March 2021
          • Received: 1 November 2020
          Published in health Volume 2, Issue 4

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