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It is widely acknowledged that vaccinating at maximal effort in the face of an ongoing epidemic is the best strategy to minimise infections and deaths from the disease. Despite this, no one has proved that this is guaranteed to be true if the disease follows multi-group SIR (Susceptible-Infected-Recovered) dynamics. This paper provides a novel proof of this principle for the existing SIR framework, showing that the total number of deaths or infections from an epidemic is decreasing in vaccination effort. Furthermore, it presents a novel model for vaccination which assumes that vaccines assigned to a subgroup are distributed randomly to the unvaccinated population of that subgroup. It suggests, using COVID-19 data, that this more accurately captures vaccination dynamics than the model commonly found in the literature. However, as the novel model provides a strictly larger set of possible vaccination policies, the results presented in this paper hold for both models.

Original publication

DOI

10.1007/s11538-023-01179-8

Type

Journal article

Journal

Bulletin of mathematical biology

Publication Date

06/2023

Volume

85

Addresses

Department of Statistics, University of Oxford, St Giles', Oxford, OX1 3LB, UK. matthew.penn@st-annes.ox.ac.uk.

Keywords

Humans, Vaccination, Models, Biological, Mathematical Concepts, Epidemics, COVID-19