Show simple item record

dc.contributor.authorDube, Michael
dc.contributor.authorHoughten, Sheridan
dc.contributor.authorAshlock, Daniel
dc.date.accessioned2022-11-09T13:42:00Z
dc.date.available2022-11-09T13:42:00Z
dc.date.issued2020-07
dc.identifier.citationM. Dubé, S. Houghten and D. Ashlock, "Modelling of Vaccination Strategies for Epidemics using Evolutionary Computation," 2020 IEEE Congress on Evolutionary Computation (CEC), 2020, pp. 1-8, doi: 10.1109/CEC48606.2020.9185662.en_US
dc.identifier.urihttp://hdl.handle.net/10464/16897
dc.description.abstractPersonal contact networks that represent social interactions can be used to identify who can infect whom during the spread of an epidemic. The structure of a personal contact network has great impact upon both epidemic duration and the total number of infected individuals. A vaccine, with varying degrees of success, can reduce both the length and spread of an epidemic, but in the case of a limited supply of vaccine a vaccination strategy must be chosen, and this has a significant effect on epidemic behaviour.In this study we consider four different vaccination strategies and compare their effects upon epidemic duration and spread. These are random vaccination, high degree vaccination, ring vaccination, and the base case of no vaccination. All vaccinations are applied as the epidemic progresses, as opposed to in advance. The strategies are initially applied to static personal contact networks that are known ahead of time. They are then applied to personal contact networks that are evolved as the vaccination strategy is applied. When any form of vaccination is applied, all strategies reduce both duration and spread of the epidemic. When applied to a static network, random vaccination performs poorly in terms of reducing epidemic duration in comparison to strategies that take into account connectivity of the network. However, it performs surprisingly well when applied on the evolved networks, possibly because the evolutionary algorithm is unable to take advantage of a fixed strategy.en_US
dc.publisherIEEEen_US
dc.rights.urihttps://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
dc.source2020 IEEE Congress on Evolutionary Computation (CEC)
dc.subjectVaccination Strategiesen_US
dc.subjectEvolutionary Computationen_US
dc.subjectContact networken_US
dc.titleModelling of Vaccination Strategies for Epidemics using Evolutionary Computationen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/cec48606.2020.9185662
refterms.dateFOA2022-11-09T13:42:00Z


Files in this item

Thumbnail
Name:
2020_CEC_Vaccination_Strategie ...
Size:
292.6Kb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record