Computational models for COVID-19 dynamics prediction

dc.contributor.author	Kloczkowski, A.
dc.contributor.author	Fernández Martínez, Juan Luis
dc.contributor.author	Fernández Muñiz, María Zulima
dc.date.accessioned	2024-02-26T07:38:39Z
dc.date.available	2024-02-26T07:38:39Z
dc.date.issued	2023
dc.identifier.isbn	978-303142507-3
dc.identifier.issn	0302-9743
dc.identifier.uri	https://hdl.handle.net/10651/71585
dc.description	22nd International Conference on Artificial Intelligence and Soft Computing, ICAISC (22nd. 2023. Zakopane, Poland)
dc.description.sponsorship	AK acknowledges the financial support from NSF grant DBI 1661391, and NIH grants R01GM127701, and R01HG012117.
dc.format.extent	p. 228-238
dc.language.iso	eng
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rights	© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
dc.source	Scopus
dc.source.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174451484&doi=10.1007%2f978-3-031-42508-0_21&partnerID=40&md5=46f7045ebc66da36831c799bf9ba607f
dc.title	Computational models for COVID-19 dynamics prediction
dc.type	conference output
dc.identifier.doi	10.1007/978-3-031-42508-0_21
dc.relation.publisherversion	http://dx.doi.org/10.1007/978-3-031-42508-0_21

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