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Vaccinating a population is a changing programming problem
2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1759018
ABSTRACT
How best to apply vaccines to a population is an open problem. It is trivial to derive intuitive strategies, but until tested, their efficacy i s n ot k nown. T his p roblem i s particularly challenging when considering the dynamics of social contact networks and their changes over time. A system for automatically discovering tested vaccination strategies with evolutionary computation has been improved upon to include additional graph metrics and to generate vaccination strategies for dynamic graphs, something that is expected of real social networks within communities. The system’s ability to generate effective strategies was demonstrated along with a comparison of the strategies developed when fit t o a s tatic g raph v ersus a d ynamic g raph. I t w as observed that the additional computational resources required to generate strategies on a dynamic graph may not be necessary as strategies developed for static graphs performed similarly well;however, the authors are careful to acknowledge that results may differ significantly w hen a djusting t he s ystems m any parameters. © IEEE 2021.
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Full text: Available Collection: Databases of international organizations Database: Scopus Topics: Vaccines Language: English Journal: 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2021 Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Topics: Vaccines Language: English Journal: 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2021 Year: 2021 Document Type: Article